Byzantine-resilient, asynchronous, yet decentralized federated learning #5221

synctext · 2020-03-18T10:44:03Z

Key points:

Cum Laude ambitions
thesis science must come above everything else
possible direction with most science imaginable
TBM compatible; thus governance of the global financial system (who owns capitalism)
single universal global market
- liquidity attracts liquidity
- near-zero cost trust
- inherently superior and open
- by design hostile to speculators, hft, and any middleman
- mechanism design against speculation tax
- market makers redundant?
startup use-case for bootstrapping liquidity
- real euros and real dollar plus Bitcoins?
- real mortgage market?
- avoid utopia thinking
- re-use existing infrastructure from the lab
thousands of DAO/blockchain competing projects, with fancy white papers and high ambition level
integrity of the market : fakes, extortion, and fraud
companies become a front-end for shared critical infrastructure, they keep their helpdesk and customers. However, they can do deals with customers from competitors in a single highly liquid market
entirely non-custodial, no proxy tricks, autonomous-entities-only principle
market consolidation and domination mechanism
- design some principle that Booking.com, Expedia and Airbnb see that this is an existential threat and they decide to all join, collaborate, and extend the global market; instead of being cut-off.
- Why did The Internet eat all other proprietary communication protocols? re-use that trick!
- Booking.com, Expedia and Airbnb: they sort of become ISPs for the global market infrastructuur.
- disruptive change: everything becomes transparent and open; pricing, utilisations, market share, etc.
game theory: fully self-interest driven
- no helping others for free
- willing to do effort for your own interest, safety and fraud prevention
- maintenance?
- Delft University should not be special; always ability to fork away; keep everybody honest

devos50 · 2020-03-18T11:07:02Z

Interesting thesis direction!

We aim to achieve this goal with our own decentralized exchange, which is fundamentally different from existing DEXes. I think the OP identified the main flaws of existing DEXes (e.g., BitShares, Waves, OasisDEX...). Even though the idea of DEXes is interesting, their liquidity is too low to attract traders. Furthermore, even though they offer trust-less asset settlement, their (transaction) costs are still high (of course, this also depends on the specifications of the underlying protocols). Finally, there are many fairness issues attached to blockchain-based exchanges.

I think market fairness as central thesis component could be a viable and novel research direction. This ties directly into mechanism design and behavior of individual agents, whose goal is to optimize their own profits. Can we make a market that provides fairness to traders? Fairness is a multi-dimensional property so you might want to focus on a specific aspect of fairness.

synctext · 2020-03-18T13:46:49Z

Some overlap here, however they just want their own coin to become big https://docs.bisq.network/dao/phase-zero.html

Bisq, a peer-to-peer exchange network designed for secure, private and
censorship-resistant trading of bitcoin for national currencies and other cryptocurrencies
[snip]
Appendix A: Roles defines the roles individuals play when participating in the Bisq DAO,
such as trader, contributor and stakeholder, and defines several categories of high-trust
bonded contributor roles such as maintainer, operator, and administrator;

jverbraeken · 2020-04-07T14:22:53Z

Initial exploratory literature review:
(a) digital marketplaces (https://fetch.ai/wp-content/uploads/2019/10/Fetch.AI-Economics-white-paper.pdf)
(b) distributed ledgers used for financial purposes (http:https://www.smallake.kr/wp-content/uploads/2018/09/E_JPX_working_paper_No15.pdf / https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3352293)
(c) peer-to-peer lending (https://www.oecd.org/daf/ca/The-Potential-for-Blockchain-in-Public-Equity-Markets-in-Asia.pdf)

jverbraeken · 2020-04-18T18:19:14Z

Initial thoughts after reading some literature, before discussing with Johan:

One universal platform on which every supply/demand-market can be built (from Uber to Airbnb, from Amazon to stock markets, from insurers to lenders)
Everything peer-to-peer to prevent extra costs from middlemen
Goal = mainstream adoption ===> integration of payments methods such as Paypal and creditcards is essential + governments should be able to regulate the platform within their regions
The throughput necessary will easily (!) exceed 9000 transactions/second. _{^{For trading in USA alone, system should be able to handle 800 million trades/day => 9000 per second}}
Still struggling with who's going to pay the electricity bills

The idea is that anyone can create a market. I identified several considerations that a market creator should consider:

Checks before the market can be created
- Government regulations (stocks, airbnb, uber)
  - A partitioned scheme that keeps certain transactions within certain locations may be required to satisfy local jurisdictional requirements
At creation time
- Send potential buyers/sellers amessage when new marketplace has opened?
  - E.g. notify a bank when someone wants to borrow
- Digital or physical product?
- Semantic links with other markets (e.g. ubers in Delft subset of ubers in South-Holland, black ubers subset of ubers)
At run-time
- Buyer
  - Anonymous or not?
    - Anonymity of both parties is crucial for stock markets, but undesirable for airbnb markets
- Product
  - Scheduling of the good? (airbnb)
    - Can change every now and then
  - Location of the good? (uber / airbnb)
    - Can change every now and then
  - Order book
    - Type of matching
      - Mostly relevant for stock exchanges: first-in-first-out or pro-rata?
    - Fixed price level or not?
    - Can you see the other asks/bids or not?
      - On stock markets this is desirable, on Amazon this is unnecessary
    - Additional service
      - Reviews for the product or not?
        
        Does not make sense for the stock market, but very valuable for airbnb
        
        How to handle fake reviews?
      - Recommended items?
- Seller
  - Anonymous or not?
    - Anonymity of both parties is crucial for stock markets, but undesirable for airbnb markets
At purchase time
- KNY/AML/CTM? (know your customer / anti-money laundering / counter-terrorism financing)
- (for physical goods: ) choose third-party escrow to provide protection?
- Payment details
  - Automatic / manual approval of purchase
    - E.g. does airbnb owner want to have the renter
  - Collatoral or not? (house, car)
  - Payment obligation (paying right now, in terms, physical or monetary goods, crypto or fiat, …)
- Conditions to enter a marketplace
  - E.g. age for adult stuff or creditworthiness for loans
  - For loans we still need creditworthiness for external party
    - In USA they have a system with bad privacy
    - In Netherlands based on financial situation and loan payment history
    - Additional details may be obligatory to deploy bailiff (incassobureau/deurwaarder)
After purchase
- Should some party by notified to keep track of the order?
  - E.g. corporate share register managed by a transfer agent for stock markets
- Cancellation possibility after the item has already been purchased
  - _{^{"The ability for clients to correct/cancel/adjust transactions that were inadvertently charged or credited to the wrong account is a common occurrence and well managed by today’s financial institutions. Additionally, complex financial transactions often include the ability to reverse the transaction based on contractual stipulations as a desirable feature. The ability to cancel or reverse a transaction is not supported in today’s distributed ledger platform, and it is not clear today how the platform could evolve to support that"}}
- Vendor obligation (sending product right now, in terms, lottery, …)

synctext · 2020-04-19T08:46:03Z

great taxonomy start! Trustchain is consensus-free, thus no electricity for mining.
What are two isolated markets that can be merged into a single infrastructure? Preferably with low competition, high fees, low innovation, and incentives to use your open alternative.

synctext · 2020-04-19T13:27:57Z

Brainstorm... Inspired by this payout platform it would be possible to build market trackers(). A payout is provided if the stock market is either below or above a certain mark. Use our DAO #5143 with shared ownership of Bitcoins.

currency trading: Euro/Dollar rate prediction market + actual exchange, clearance and settlement.
largest: new york stock exchange tracker
long-term public trackers
Prediction market for climate change: Prediction market for climate change #2882 (everybody can invest against/on climate change within minutes. This is impossible today. our 2017 idea)
food pricing ???

devos50 · 2020-04-19T13:37:59Z

I am currently working on a classification/overview of existing electronic market/trade mechanisms based on blockchain technology (for a possible paper). This also includes literature on prediction markets, like Augur. I have collected 130+ (mostly peer-reviewed) articles so far and categorised them. I think there is quite some overlap with topics that you brought up. If you are interested in them, I can share them with you 👍

synctext · 2020-04-19T16:57:56Z

can you post a list here? + URLs possible even...

devos50 · 2020-04-19T17:11:14Z

@synctext sure! I'm using the Papers application to organize and categorize all the literature that I read, and I'm currently in the process of reading/summarizing each paper. I think the list below is quite complete regarding academic work, but there are still a few open-source (non-peer-reviewed) implementations missing.

Atomic Swaps

Alt chains and atomic transfers

Atomic Cross-Chain Swaps.

Atomic Cross-chain Swaps - Development, Trajectory and Potential of Non-monetary Digital Token Swap Facilities.

Atomic Cross-Chain Swaps with Improved Space and Time Complexity.

Atomic Crosschain Transactions for Ethereum Private Sidechains.

Atomic Swaptions: Cryptocurrency Derivatives

Cross-chain Deals and Adversarial Commerce.

Extending Atomic Cross-Chain Swaps.

On the optionality and fairness of Atomic Swaps.

On the specification and verification of atomic swap smart contracts.

Privacy-Preserving Cross-Chain Atomic Swaps

The state of atomic swaps

(via @PapersApp)
http:https://scholar.google.comjavascript:void(0)
https://dblp.org/rec/conf/podc/Herlihy18
http:https://aetic.theiaer.org/archive/v3/v3n1/p5.html
https://dblp.org/rec/journals/corr/abs-1905-09985
https://dblp.org/rec/journals/corr/abs-1904-12079
http:https://arxiv.org/abs/1807.08644v2
https://dblp.org/rec/journals/pvldb/HerlihySL19
http:https://link.springer.com/10.1007/978-3-030-31500-9_14
http:https://dl.acm.org/citation.cfm?doid=3318041.3355460
http:https://arxiv.org/abs/1811.06099v1
http:https://fc20.ifca.ai/wtsc/WTSC2020/WTSC20_paper_20.pdf
http:https://diyhpl.us/wiki/transcripts/scalingbitcoin/tokyo-2018/atomic-swaps/

Auctions

PASTRAMI: Privacy-preserving, Auditable, Scalable&Trustworthy Auctions for Multiple Items

Succinctly Verifiable Sealed-Bid Auction Smart Contract.

Verifiable Sealed-Bid Auction on the Ethereum Blockchain.

(via @PapersApp)
http:https://link.springer.com/10.1007/978-3-030-00305-0_1
http:https://link.springer.com/10.1007/978-3-662-58820-8_18

Custodial Exchanges

Bitcoin: Economics, Technology, and Governance

Bitcoin Transaction Malleability and MtGox

Making Bitcoin Exchanges Transparent

Provisions: Privacy-preserving Proofs of Solvency for Bitcoin Exchanges

ShapeShift

Tesseract - Real-Time Cryptocurrency Exchange Using Trusted Hardware.

The Arwen Trading Protocols (Full Version).

Why Preventing a Cryptocurrency Exchange Heist Isn’t Good Enough

(via @PapersApp)
http:https://pubs.aeaweb.org/doi/10.1257/jep.29.2.213
https://link.springer.com/chapter/10.1007/978-3-319-11212-1_18
https://link.springer.com/chapter/10.1007/978-3-319-24177-7_28
http:https://dl.acm.org/citation.cfm?doid=2810103.2813674
https://shapeshift.io
http:https://dl.acm.org/doi/10.1145/3319535.3363221
https://dblp.org/rec/journals/iacr/HeilmanLG20
https://link.springer.com/chapter/10.1007/978-3-030-03251-7_27

Decentralized Exchanges

0x: An open protocol for decentralized exchange on the Ethereum blockchain

A Demonstration of Sterling - A Privacy-Preserving Data Marketplace.

A Distributed Digital Asset-Trading Platform Based on Permissioned Blockchains

Beaver - A Decentralized Anonymous Marketplace with Secure Reputation.

Bisq - The peer-to-peer Bitcoin Exchange

BitShares 2.0: General Overview

Coincer: Decentralised Trustless Platform for Exchanging Decentralised Cryptocurrencies

Decentralized blockchain-based electronic marketplaces

Decentralizing the Stock Exchange using Blockchain An Ethereum-based implementation of the Bucharest Stock Exchange.

Deconstructing Decentralized Exchanges

Enigma Catalyst : A machine-based investing platform and infrastructure for crypto-assets

Etherdelta

Fast and secure global payments with Stellar

Fragmentation of Distributed Exchanges

IDEX: A Real-Time and High-Throughput
Ethereum Smart Contract Exchange

IDMoB - IoT Data Marketplace on Blockchain.

Komodo BarterDEX

Kyber Network whitepaper

Localbitcoins

Loopring: A decentralized token exchange protocol

Mind my value - a decentralized infrastructure for fair and trusted IoT data trading.

Open bazaar protocol

Polkadot: Vision for a heterogeneous multi-chain framework

Republic Protocol: A decentralized dark pool exchange providing atomic swaps for Ethereum-based assets and Bitcoin

Resource Control in P2P Cryptocurrency Networks

SmartExchange: Decentralised Trustless Cryptocurrency Exchange

Swap: A Peer-to-peer Protocol for Trading Ethereum Tokens

Waves.Exchange | Buy crypto with 0% fees

XCLAIM: Trustless, Interoperable, Cryptocurrency-Backed Assets

(via @PapersApp)
https://static.xcj.com/uploads/20180518/xumb4xmv3o1516701172833.pdf
http:https://dl.acm.org/citation.cfm?doid=3229863.3275603
https://link.springer.com/chapter/10.1007/978-3-030-05764-0_6
https://dblp.org/rec/journals/iacr/SoskaKCD16
https://docs.bisq.network/exchange/whitepaper.html
https://cryptorating.eu/whitepapers/BitShares/bitshares-general.pdf
https://link.springer.com/chapter/10.1007/978-3-319-64701-2_53
https://dl.acm.org/doi/10.1145/3158333
https://ieeexplore.ieee.org/document/8516610/
https://assets.pubpub.org/ob89i66u/61573938834913.pdf
https://etherdelta.com
http:https://dl.acm.org/citation.cfm?doid=3341301.3359636
http:https://arxiv.org/abs/1910.11216v2
https://ieeexplore.ieee.org/document/8525388/
https://files.kyber.network/Kyber_Protocol_22_April_v0.1.pdf
https://localbitcoins.com
https://raw.githubusercontent.com/Loopring/whitepaper/master/en_whitepaper.pdf
http:https://dl.acm.org/citation.cfm?doid=3131542.3131564
http:https://docs.openbazaar.com
http:https://scholar.google.comjavascript:void(0)
http:https://arxiv.org/abs/1810.11675v1
https://link.springer.com/chapter/10.1007/978-3-030-04849-5_32
https://swap.tech/whitepaper/
https://waves.exchange
https://ieeexplore.ieee.org/document/8835387/

Blockchain-based Energy Trading

Blockchain-based electricity trading with Digitalgrid router

Consortium Blockchain for Secure Energy Trading in Industrial Internet of Things.

Crypto-trading: Blockchain-oriented energy market

Energy trading for fun and profit buy your neighbor's rooftop solar power or sell your own-it'll all be on a blockchain

Implementation of blockchain-based energy trading system

Privacy-Preserving Energy Trading Using Consortium Blockchain in Smart Grid.

Security and Privacy in Decentralized Energy Trading Through Multi-Signatures, Blockchain and Anonymous Messaging Streams.

Towards resilient networked microgrids: Blockchain-enabled peer-to-peer electricity trading mechanism

(via @PapersApp)
https://ieeexplore.ieee.org/abstract/document/7991065/
http:https://ieeexplore.ieee.org/document/8234700/
https://ieeexplore.ieee.org/abstract/document/8240547/
https://ieeexplore.ieee.org/abstract/document/8048842/
https://www.emerald.com/insight/content/doi/10.1108/APJIE-12-2017-037/full/html
https://ieeexplore.ieee.org/document/8613816/
https://dblp.org/rec/journals/tdsc/AitzhanS18
https://ieeexplore.ieee.org/abstract/document/8245449/

Matchmaking

0x: An open protocol for decentralized exchange on the Ethereum blockchain

Etherdelta

Libra - Fair Order-Matching for Electronic Financial Exchanges.

Loopring: A decentralized token exchange protocol

Swap: A Peer-to-peer Protocol for Trading Ethereum Tokens

The cost of decentralization in 0x and EtherDelta

(via @PapersApp)
https://static.xcj.com/uploads/20180518/xumb4xmv3o1516701172833.pdf
https://etherdelta.com
http:https://dl.acm.org/citation.cfm?doid=3318041.3355468
https://raw.githubusercontent.com/Loopring/whitepaper/master/en_whitepaper.pdf
https://swap.tech/whitepaper/
https://hackingdistributed.com/2017/08/13/cost-of-decent/

Prediction Markets

A permissioned blockchain-based implementation of LMSR prediction markets.

A Smart Contract Oracle for Approximating Real-World, Real Number Values

Augur: a decentralized, open-source platform for prediction markets

Decentralized Prediction Market Without Arbiters.

Gnosis whitepaper

(via @PapersApp)
https://linkinghub.elsevier.com/retrieve/pii/S016792361930257X
https://drops.dagstuhl.de/opus/volltexte/2020/11970
http:https://cryptoverze.com/wp-content/uploads/2019/01/augur.pdf
http:https://link.springer.com/10.1007/978-3-319-70278-0_13
http:https://scholar.google.comjavascript:void(0)

Security Aspects of Trading

Flash Boys 2.0 - Frontrunning, Transaction Reordering, and Consensus Instability in Decentralized Exchanges.

Footprints on a Blockchain: Trading and Information Leakage in Distributed Ledgers

On the impossibility of fair exchange without a trusted third party

On the optionality and fairness of Atomic Swaps.

Provisions: Privacy-preserving Proofs of Solvency for Bitcoin Exchanges

The cost of decentralization in 0x and EtherDelta

The Decentralized Financial Crisis: Attacking DeFi

Zexe - Enabling Decentralized Private Computation.

(via @PapersApp)
https://dblp.org/rec/journals/corr/abs-1904-05234
http:https://jot.pm-research.com/lookup/doi/10.3905/jot.2017.12.3.005
https://pdfs.semanticscholar.org/208b/22c7a094ada20736593afcc8c759c7d1b79c.pdf
http:https://dl.acm.org/citation.cfm?doid=3318041.3355460
http:https://dl.acm.org/citation.cfm?doid=2810103.2813674
https://hackingdistributed.com/2017/08/13/cost-of-decent/
http:https://arxiv.org/abs/2002.08099v1
https://dblp.org/rec/journals/iacr/BoweCGMMW18

Settlement Mechanisms

A protocol for interledger payments

Atomically Trading with Roger - Gambling on the Success of a Hardfork.

Blockchain-based secure digital asset exchange scheme with QoS-aware incentive mechanism.

Blockchain-based settlement for asset trading

Blockchain router: A cross-chain communication protocol

Bootstrapping a Blockchain Based Ecosystem for Big Data Exchange.

Centrally Banked Cryptocurrencies.

Cross-asset trading within blockchain networks

Cross-Chain Payment Protocols with Success Guarantees

DeXTT - Deterministic Cross-Blockchain Token Transfers.

Escrow Protocols for Cryptocurrencies - How to Buy Physical Goods Using Bitcoin.

Fair and Decentralized Exchange of Digital Goods.

Fair Two-Party Computations via Bitcoin Deposits.

FairSwap - How To Fairly Exchange Digital Goods.

Hallex: A trust-less exchange system for digital assets

How to Use Bitcoin to Design Fair Protocols.

Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchains

HyperPubSub - Blockchain Based Publish/Subscribe.

Implementing an Asset Trading System Based on Blockchain and Game Theory

Liquid Speed: On-Demand Fast Trading at Distributed Exchanges

Market design for trading with blockchain technology

On the impossibility of fair exchange without a trusted third party

Optimistic Protocols for Fair Exchange.

PEX - Privacy-Preserved, Multi-Tier Exchange Framework for Cross Platform Virtual Assets Trading.

Pisa - Arbitration Outsourcing for State Channels.

Proof of Delivery of Digital Assets Using Blockchain and Smart Contracts.

Real-time Money Routing by Trusting Strangers with your Funds.

Resource trading in blockchain-based industrial Internet of Things

SDTE - A Secure Blockchain-Based Data Trading Ecosystem.

Towards Decentralized Equilibrium Asset Trading Based on Blockchain.

Trading Real-World Assets on Blockchain - An Application of Trust-Free Transaction Systems in the Market for Lemons.

Trading Stocks on Blocks - Engineering Decentralized Markets

Trust Is Risk - A Decentralized Financial Trust Platform.

Usable optimistic fair exchange.

XChange - A Blockchain-based Mechanism for Generic Asset Trading In Resource-constrained Environments.

XCLAIM: Trustless, Interoperable, Cryptocurrency-Backed Assets

Various

Attacking the DeFi Ecosystem with Flash Loans for Fun and Profit.

Beware the Middleman - Empirical Analysis of Bitcoin-Exchange Risk.

Blockchain-enabled Intelligent Asset Exchange for a Circular Economy.

Challenges and Opportunities Associated with a Bitcoin-Based Transaction Rating System.

Dispute Resolution for Smart Contract-based Two-Party Protocols.

Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchains

Measuring the Longitudinal Evolution of the Online Anonymous Marketplace Ecosystem.

Overview of Emerging Blockchain Architectures and Platforms for Electronic Trading Exchanges

Peer Review - Toward a Blockchain-enabled Market-based Ecosystem.

Shared Ledger Accounting - Implementing the Economic Exchange pattern.

The Economics of Cryptocurrency Pump and Dump Schemes

The Emerging Role of Electronic Marketplaces on the Internet.

Tokenization: Open Asset Protocol on Blockchain

Towards atomic cross-chain token transfers: State of the art and open questions within tast

(via @PapersApp)
http:https://arxiv.org/abs/2003.03810v2
http:https://link.springer.com/10.1007/978-3-642-39884-1_3
https://dblp.org/rec/journals/ercim/AskoxylakisAD17
http:https://link.springer.com/10.1007/978-3-662-44774-1_3
https://ieeexplore.ieee.org/document/8751312/
http:https://dl.acm.org/citation.cfm?doid=3190508.3190538
https://dblp.org/rec/conf/uss/SoskaC15
http:https://www.ssrn.com/abstract=2867344
https://dblp.org/rec/journals/cais/Avital18
https://linkinghub.elsevier.com/retrieve/pii/S0306437919304892
https://papers.ssrn.com/abstract=3310307
http:https://portal.acm.org/citation.cfm?doid=280324.280330
https://ieeexplore.ieee.org/abstract/document/8711021/
https://dsg.tuwien.ac.at/projects/tast/pub/tast-white-paper-1.pdf

jverbraeken · 2020-04-20T13:58:24Z

Thanks a lot for the suggestions @synctext @devos50 👍🏻👍🏻
I hadn't heard of Augur yet, very interesting project indeed! Will change my proposed taxonomy a bit to allow for prediction markets.
Would be great if you could share your summaries of those papers @devos50!
My holiday is over unfortunately, from July onwards I'll dedicate all my time to this thesis project

jverbraeken · 2020-04-20T14:02:01Z

great taxonomy start! Trustchain is consensus-free, thus no electricity for mining.
What are two isolated markets that can be merged into a single infrastructure? Preferably with low competition, high fees, low innovation, and incentives to use your open alternative.

I'd need to look more into that. At first sight, I'd say that Tinder is a great example since they have a near-monopoly, very high fees, and close to no innovation.

devos50 · 2020-04-20T14:05:28Z

I'd need to look more into that. At first sight, I'd say that Tinder is a great example since they have a near-monopoly, very high fees, and close to no innovation.

See Matchpool: dating on a blockchain

You can collect 'arrows' by proposing love interests. You are rewarded with more arrows as the relationship advances and gets more intimate 😄.

jverbraeken · 2020-04-20T14:07:25Z

Haha nicee, awesome project 😂😂

synctext · 2020-07-16T06:59:41Z

Coming 6 weeks:

come up with wow-factor stuff
- Example: an open market for tax evasion for normal people
- now only the elite can do tax minimisation. Always use a proxy. At least 200 private jet aircraft have been imported through "the Isle of Man" and cut tax bills by £790m ($1bn).
- digitize the Vermont-EU private tax-free pension buildup or the https://en.wikipedia.org/wiki/Paradise_Papers proxies for zero-taxation
- lega online market for services which minimize your tax through loopholes.
- Moral high-ground: grow political will to plug the holes that the elite exploits.
- ToDo: find an bootstrap thesis prototype and showcase market
beyond engineering, https://github.com/jverbraeken/UniversalMarket/wiki/Plan
Freeze the final direction in 3 month
it has to have scientific grounding for highest grade, novel algorithm or architecture
make markets safe: escrow, iterative payment versus delivery
make markets universal, build upon FBase "device drivers" for specific markets
build upon programmable money of Internet-of-Money IBAN integration
go beyond target2
Advise: read complex papers by the IMF and federal reserve
Fully distributed can be very fast
Focus on a core problem, like Fairswap mentioned above.

Universal market research questions from kick-off presentation:

Universal market: search in all these goods, semantics, and relevance ranking
Universal market: large storage requirement of all these product descriptions, 1 number to rule them all (everybody switch to the Joost Numbering System (JNS))
Universal market: custodians, open market, transparent, and low-cost

Existing:

jverbraeken · 2020-07-16T07:23:24Z

synctext · 2020-07-16T13:05:24Z

Please read the current status of our global AI marketplace tooling:
https://github.com/Tribler/tribler/files/4929974/Dollynator.-.Final.Report.pdf
After contributions by 31 students in various courses over the years, it is quite sophisticated. Bug-free also obviously 😉

jverbraeken · 2020-07-24T07:21:18Z

15 additional screenshots

synctext · 2020-07-24T07:30:54Z

https://github.com/jverbraeken/UniversalMarket/wiki/Points-for-consideration
Follow a structured process;
- no wow-factor! thesis direction found yet.
- read ideas of others to come up with superior smart ideas
- August: document 10+ thesis ideas in few sentences; scientific challenge (axiomatic science: the market primitive! Cheating is guaranteed to be eventually detected; infra: bitshares, waves, etc,; Matching algorithms for trades; ID: no trolls, intermediaries: trustworthy; persistence and finality: core concepts; decentralised finance: P2P lending constructs; Market leaders: Amazon,Target2; custodian: boring stuff)
- September: pick your top-3 ideas, expand them to 1 page of scientific challenge + state-of-the-art, related work.
- September 30: decide on direction.
- October: fully focus on single direction.
Decentral systems:
- latency is not a problem in (de)central systems, its about engineering excellence
- long-tail is not a problem in (de)central systems, its about engineering excellence
- right to be forgotten is not a problem in (de)central systems, its about engineering excellence
new insight: market primitives for digital markets are easier. Involving the physical world introduces many messy problems. Easier to show novel science in the digital-only markets. Transactions are atomic, fraud is easier to detect, tamper-proof logging solves honesty, incremental payments, and still markets are fragmented!
Strong disincentive to establish a universal market. No monopoly power. if the single market is created, forces companies to join it.
monopoly problems:
- http:https://andras.niedermayer.ch/wp-content/uploads/2014/08/PFS-2016-06-27-AN-SL.pdf
- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=980755
Universal market for digital goods:
- Digital services (unique goods which can't be copied)
- Steam alternative, content for gaming market
- Spotify alternative, content for music industry
- Netflix alternative, content for movie industry
- Unify: limited supply, fixed price, bid/ask, lottery, etc.

devos50 · 2020-07-30T12:46:45Z

Some (peer-reviewed) work related to your ideas: Domain Ontology for Digital Marketplaces.

jverbraeken · 2020-08-26T12:15:50Z

synctext · 2020-08-26T12:56:12Z

Ideas for thesis focus:

middleware for distributed machine learning (toward decentralised computation)
- mobile-first zero-cost AI (fully decentralised, zero-server, no upkeep cost)
- resilient against abuse (troll army)
- data never leaves mobile (only training vectors, etc)
- it simply works (its not about the 10% state-of-the-art performance)
- self-scaling (from 1 thousand to 1 billion users)
- scientific progress: select those ideas which will scale it the real world and provide the empirical evidence.
Second idea
ToDo: the imperfect scientific paper which everybody jumps upon and improves upon. Seed a community and start a platform.

jverbraeken · 2020-09-09T11:42:22Z

We choose to focus first on federated learning (instead of distributed learning), because this field is less researched and poses more significant challenges regarding incentives/privacy/security. When this works, extending the functionality to support distributed learning is fairly trivial.

Some literature:

Must-haves

Step 1

Create IPv8 overlay in SuperApp and let it run on a few servers, see:

Step 2

Successfully train a CNN on the MNIST database on a single node

Step 3

Implement most basic gossiping protocol for distributed CNN-training: https://arxiv.org/pdf/1611.04581.pdf

Create block type I: blk_parameters

Should-haves

Step 4

Prevent attacks (sybil + injection)

Strong identities by giving people €0,01 when they use the app and hashing their bank account
Peers select models by (1) taking a sample of models based on the reputation of the peers that created them, and (2) test their performance on their local data, take the 3 best performing models, and take the average of them
Letting everyone verify if contribution of a random other person improves the accuracy based on his/her own data => put this on the blockchain (RONI)

Create block type II: blk_reputation
Establish reputation based on KRUM, median, FoolsGold, ...
Problem: doesn't work well for non-IID environments, see: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9066920
Put most emphasis on most recent interactions in calculating someone's reputation
Incentive mechanism: limit functionality of app when people don't train the model
Reputation calculation also based on how well a peer evaluates other peers

Literature:

https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8832210
https://arxiv.org/pdf/1811.09904.pdf (reputation denoted as "stake")
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8994206
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9150433 (want to take similar approach, but with reputations)

Step 5

Use differentially private noise to enhance privacy

Also consider using Shamir secrets: https://arxiv.org/pdf/1811.09904.pdf
Or make all peers completely anonymous
Or apply dimensionality reduction (PCA): https://arxiv.org/pdf/1902.10799.pdf

Could-haves

Step 6

Replace SGD (that everyone uses, why???) with something way better https://ruder.io/optimizing-gradient-descent/index.html#whichoptimizertouse

Step 7

Develop a general supply/demand platform where people can request other people to train their models, people choose which models they want to train

Financial incentives
Both federated and distributed learning

Step 8

Take the average of lots of models in the early stages of the network (initially large fluctuations) and phase this averaging out when the model eventually converges

Use averaging based on weights to put more emphasis on models trained with lots of training data (FedAVG / FedSGD)

jverbraeken · 2020-09-09T12:37:50Z

Research questions:

How can we prevent sybil/injection attacks in a decentralized federated learning system?
Practically all papers use SGD to train decentralized neural networks; would better parameter optimization methods also work well in decentralized settings?
Can we replace a blockchain to store the model parameters with an eventually consistent system (trustchain) to achieve superior performance?

synctext · 2020-09-09T12:41:04Z

Now it is really secure 😆 ; key thesis selling point. Key related work: Biscotti.

Concrete thesis direction proposal:
federated learning on edge devices is a new emerging field with significant promise, but numerous unsolved challenges around privacy, security, and incentives. This thesis will significantly address the state-of-the-art by demonstrating a performance leap beyond classical Gradient Decent, security advancement without reliance on external trusted third parties or services, address the privacy issue without introducing brittle zero-knowledge proofs, and provide incentive alignment.
Our solution relies on secured gossip to address model pollution and Sybil attack by providing easy-to-use trust framework which utilises Internet latency diversity, graph analysis of user activity, and transaction properties for attack-resilience.

Concrete goal: in 6 weeks a "Poisoning attack" (Wednesday 21 Oct).
Possible future sprints past this date:

broader attacks
broader attacks and taxonomy of defence mechanisms
what dataset to use. For instance, high-impact social relevance on edge devices: Anticipating Accidents in Dashcam Videos dataset. video analysis is core of many scientific papers. Possible to release this inside Superapp, re-use code, ask volunteers to mount this in car, live network for scientific publication?
incentive alignment, wild idea: Who sees the fastest car on the road today? Detect and show how fast cars are going which overtake you on the highway. Fun, improve safety and automatically publish photos with unredacted license plates to Trustchain. Re-use like https://github.com/pddenhar/OpenCV-Dashcam-Car-Detection

jverbraeken · 2020-09-23T07:29:20Z

Update September 23

Created new superapp module
Ping-pong between 2 devices
Train MNIST dataset on single phone
Simulate distributed MNIST training on single phone
Create an faster TFTP-alternative by sending ~50 messages simultaneously (speedup: ~11x faster)
Implement weighted averaging
Train MNIST dataset distributed on 2 phones

synctext · 2020-09-23T12:28:43Z

Next step is fragmenting the dataset on 2 phones
TFTP is 40 years old, modern protocol in Java: https://github.com/iiljkic/utp4j
usage of https://github.com/eclipse/deeplearning4j/
for comparison of achieved additional performance to related work it is required to use the usual datasets and usual configs.
Focus on the test, attack emulation, and research infrastructure for 1 month?
Attacks to test
- "Honest, but curious" attack first
- injection of spam/fake attack from 1 node
- creation of numerous fake identities
Defense:
- Noise :-)
- use Trustchain, latency diversity, and transaction graph properties

jverbraeken · 2021-01-11T13:01:31Z

Update January

Simulation results ready (results below are slightly outdated, fixed several bugs in the meantime)
Huge performance improvements (+- 24h => +- 6h to run the entire simulation)
Bug fixing in TFTP/UTP => fully distributed environment working
Cannot use the emulators anymore :(:(:(

synctext · 2021-01-11T13:53:36Z

Solid results!
Already sufficient for graduation.
Now focus on boosting the core scientific contribution
Further think about February as a dedicated month for high-risk research.

jverbraeken · 2021-01-26T22:52:21Z

Update January 27th

Finally got it working on the Zulu server => first results with 50 nodes (note: training takes 24 hours for test with 50 nodes, undetermined but significantly longer time for most complex model with 250 nodes). Unfortunately, the results don't seem to make sense, so have to do some bug fixing...
Also finished first distributed test with the maximum number of 16 nodes. Performance is quite a bit lower than in the non-distributed setting. Again lots of debugging needed
Finished significant part of my actual thesis
Determined research direction for coming weeks: achieving good performance in non-i.i.d. settings. A trivial rehearsal strategy yields okay-ish results, but the performance is not excellent. Techniques such as Elastic Weight Consolidation, SI, AR1, or AR* are all dependent on advanced custom loss functions (something that the library DeepLearning4j does not support :(:(:( ). However, it would very interesting to investigate other methods such as CWR+ and Generative Replay. These performance of these methods has never been investigated in a federated setting before and should significantly better performance than the methods currently being used (namely Rehearsal-based methods and EWC)

Thesis.pdf

synctext · 2021-01-27T12:53:30Z

Solid status
- simulation 250 nodes
- emulation: 16 nodes
Highest level of Thesis work is an accept in a top venue
- build upon the fact that it actually works!
- "Class A": https://sigir.org/sigir2021/call-for-demonstrations/index.html
Private Set intersection cardinality is a performance killer, excursively usable in theory.
How to combine strong byzantine fault tollerant, non i.i.d. data and offer decent performance?
1. ignore outliers?
2. Magic function to determine truth versus spam?
3. federated learning: unique local dataset. You have local knowledge of what is correct, trustworthy, spam and fake.
4. probabilistic? We can't identify bad actors, but we assume to be able to do some estimation. Use a DAG which indicates the trustworthiness of nodes.
5. Above is silly idea: we are in supervised learning setting, we can test incoming models for superiority, replace our own inferior model and thus iterate towards perfection.
6. Bootstrap vulnerability: no local data yet means you cant identify spam or adversarial data

jverbraeken · 2021-02-27T17:11:58Z

Update February 29th

https://www.tudelft.nl/en/student/eemcs-student-portal/education/graduation-msc/green-light

Ontzettend goede performance op het MNIST dataset in non-i.i.d. setting. Absoluut een grote stap vooruit t.o.v. de huidige state-of-the-art
Om te trainen op non-i.i.d. datasets gebruikt Pro-BRISTLE o.a. "deep transfer". Hiervoor moeten we veronderstellen dat er altijd wel een dataset beschikbaar is dat "een beetje lijkt" op het dataset dat de gebruiker wil trainen. Een "ongewenst" 🙈🙈🙈 bijeffect van deze techniek is dat het netwerk orders van grootte sneller leert met slechts een fractie van de netwerkcommunicatie. De performance van Pro-BRISTLE is hiermee totaal absurd veel beter dan die van de huidige state-of-the-art, maar dit komt dus door een "oneerlijk" voordeel: het gebruiken van een ander vergelijkbaar dataset.
=> daarom ga ik eerst de performance van de huidige state-of-the-art algoritmes laten zien. Daarna modificeer ik deze algoritmes met deep transfer en laat ik zien dat ze véél beter presteren. Daarna voeg ik resultaten van Pro-BRISTLE toe en laat ik zien dat dit algoritme in non-i.i.d. omgevingen en/of een asynchronous omgeving en/of een Byzantine omgeving aanzienlijk beter presteert
Heb Pro-BRISTLE nog niet aan de praat gekregen voor meer complexe datasets dan MNIST... Batch normalization lijkt het probleem te zijn
Obstakel is de tijd die nodig is om experimenten te runnen. Voor CIFAR-10 minimaal 24 uur op Zulu, voor goede performance nog langer
Implementeren van deep transfer is vrij tijdrovend, omdat ik ook een vergelijkbaar dataset voor elk dataset moet toevoegen en hierop heel goede performance MOET halen omdat elke error uiteindelijk in het eindresultaat x2 terugkomt :(
75% accuracy gehaald op het WISDM dataset. Deze (of Mobi-Act) wil ik gebruiken voor deep transfer naar HAR (Human Activity Recognition). Kleine issues met verschillende sampling rates en vervuilde datasets
Helemaal happy met Zulu :)

jverbraeken · 2021-03-01T13:20:23Z

Thesis.pdf

jverbraeken · 2021-03-15T13:15:53Z

Thesis.pdf

synctext · 2021-03-15T13:33:26Z

PSI is vulnerable against brute force item discovery: https://arxiv.org/abs/2004.03976
Fully connected component assumption, if you put a high PSI threshold
Quick related work search on federated learning and non-iid data:
- http:https://proceedings.mlr.press/v119/karimireddy20a.html (2020)
- https://arxiv.org/abs/1806.00582 (2018)

devos50 · 2021-03-29T11:45:47Z

I did a high-level screening of your thesis. Great work and good content! Below you can find my comments:

Overall major feedback:

You are too humble with your contributions. Use language like: “We improved all key aspects of decentralized federated learning, presenting a realistic, attack-resilient solution for the first time. We envision broad use of our novel algorithms, bla bla etc. We experimentally demonstrate that Pro-Bistle is highly scalable and attack-resilient compared to state-of-the-art solutions”.
Focus more on your contribution instead of enumerating all other work performed by others.
(maybe something for discussion) you do a nice job of introducing ML and its role in society, but chapter 2 demands a lot from the reader (e.g., familiarity with concepts like convergence). Since the field is so broad and extensive, it is challenging to find the right balance between briefness and accuracy.
You are explaining many, many algorithms in Chapter 2 which makes me feel lost as reader. Since your contribution is not fleshed out yet, it makes me wonder where this story goes. Adding hints to your solution would help with this.
Formulating your research (sub)questions to the end of the introduction would improve the structure of your thesis. You can refer to these RQs in Chapter 2 and 3 and summarise in your conclusion how you addressed these RQs.
Your thesis definitely improves state-of-the-art solutions but it is very hard to maintain a mental overview of all components, how they address the problems, and how they are inter-connected.

Overall minor feedback:

Some references are broken.
Make the spelling of Pro-Bristle consistent (this would be easier in LaTeX since you can write a macro for that).

Subtitle:

new -> novel (the word ‘new’ is slightly informal in this context)

Intro:

the first sentence of 1.1 is slightly misleading: you start with explaining statistics while the caption suggests that the paragraph is about machine learning.
abbreviations like i.e. and e.g. should always be followed by a comma (so: i.e., its bias)
1.1 would benefit from some citations
(nit) I personally don’t like using citations as nouns.
“Why federated learning” should be a subsection I guess?
Don’t use a quote as a full sentence (“Federated Learning brings the code …”)
You repeated the parameter server explanation in 1.3 (it is already introduced in 1.2)
I would like to see trade-offs when describing some of the design principles in 1.3.
Suggestion: “ruined by” -> “subverted by”
It is common to fully spell out numbers below twenty (3 major reasons -> three major reasons)
Avoid using one-sentence paragraphs.
Elaborate on what ‘synchronous’ and ‘asynchronous’ exactly means in this context.
(nit) don’t use the character x to indicate a variable if you don’t use it later.
Overall, I think section 1.3 (asynchronous) can be compressed.
Stalled processes -> stale processes?
You haven’t explained what i.i.d is but you refer to it in 1.4. Please elaborate it earlier (as a design principle I guess?)
“There are several types of machine learning …” I feel that this sentences does not add much. P.lease elaborate on your solution instead and position your work accordingly later in the thesis.
I also think you can merge 1.4 and 1.5 into “Key Contributions and Thesis Outline”

Chapter 2:

“is commonly used here take an empirically” there is something wrong with this sentence.
I would consider adding a figure showing a parameter server vs. not using a parameter server.
You could structure 2.3 by using bold words at the beginning of your subsections (e.g., Krum - bla bla). Even though there a transition between paragraphs, it feels like a wall of text. More structure would also enable more familiar readers to skip descriptions they are familiar with.
Not sure if this is common for Word documents, but you should add a label/caption to figures so you can reference to it. Figures feel detached from the text.

Chapter 3:

Maybe make your assumptions (which are now at the start of Chapter 3) more explicit by adding a section ‘system model and assumption’. Here you can also state other characteristics of the target environment, e.g., the reliability of communication channels.
“This part of Pro-Bristle is complex enough” -> suggestion: rephrase it to “For presentation clarity, we describe this part of Pro-Bristle in Section X”.
In the “Non-i.i.d components” subsection, you talk about detection of Byzantine behaviour. Isn’t your goal to prevent it (which is a harder problem)?
“These challenges are addressed … as illustrated in Figure 3. First, we take a step back”. Why take a step back? Don’t leave the reader hanging, please explain the Figure!
Your pseudocode is not explained at all. Use line numbers and mark interesting parts.

Chapter 4:

Just a few citations suffice to convince that MNIST is frequently used to evaluate algorithms in this domain.
The threat model is something you should discuss earlier since it influences the design of your solution.
Maybe 4.9 is more suitable to go to the ‘conclusion’ section (e.g., add a ‘reflection’ section). Great read though, I highly appreciate these discussions.

Chapter 5:

(nit) Why use these pink/black boxes instead of subsections?
Phrase the first sentence of Chapter 5 more positively, e.g., “Due to the unique properties of Pro-Bristle, it is challenging to directly compare it to existing methods. However, …”
“The number of parameters is obviously …” suggestion: to structurally evaluate the extensive parameter space of our solution, we …”.
Maybe you can make a table listing the default experiment parameters.
I have never seen a legend at the bottom of the page, and the figures not attached to it.
This chapter would benefit from subsections and more structure in general.

jverbraeken · 2021-03-29T11:47:15Z

Thesis.pdf

synctext · 2021-03-29T12:25:41Z

Thesis comment: reader is now lost in details. Problem Decription chapter with high level view: all 7 aspects of ML are needed. Use TUDelft official format. Move Chapter 3 "main contribution of this work" to chapter 1 briefly.
Mainstream ML research from this month in Nature. The EleutherAI: open project alternative. Use 7 unique points in pro-bistle that you have for organising prior work. Present giant table like these two:

jverbraeken · 2021-04-19T07:08:33Z

Thesis.pdf

synctext · 2021-04-19T09:43:37Z

Great table on page 22. Recommend using this a central element for your storytelling in earlier pages
It took roughly few months to get the complete environment for simulation (with Android).
investigate other ML thesis work at Delft

devos50 · 2021-04-28T13:17:13Z

Given these two accepted Middleware’20 papers, I believe your topic falls within the conference scope. IIRC, the FLEET paper did win the best paper award. Given that the conference season is ending, this might be your best shot for a submission, and closer to our expertise. If you are serious about submitting to Middleware, we could consider having weekly or bi-weekly feedback rounds.

Some general comments on the current Overleaf paper draft and particularly the first pages (this work is going to require more polish, please be prepared to dedicate most of your time on this the coming weeks):

Write for your audience. Motivation can and should be shorter. Given the Middleware audience, you do not need to motivate the need for machine learning, reviewers should know that.
Please thoroughly read and analyse the FLEET paper; I think the experimental evaluation and methodology is very polished.
Emphasise on the mobile, lightweight aspect of your solution (e.g., energy consumption and communication costs).
Add some exciting (positive) numbers to the last paragraph of your abstract (where you describe the experiments), e.g., Pro-Bristle achieves a X times increase in accuracy, while achieving lower energy consumption compared to existing solutions.
Highly recommended to add a figure on page 2, visualising your overall architecture and contributions.
The reviewers that bid on your paper likely are familiar with centralised FL and not with decentralised FL. What makes it different and why do we need to get rid of the centralised server (this is one of the first questions a reviewer might have)?
It seems that the focus is on 1) Byzantine resilience and 2) accuracy in non-iid environments. If they are the main selling points, they can be further elaborated and emphasised.
Position your work accordingly with prior Middleware papers on similar topics. You can already do this in the introduction.

I leave more specific feedback for later iterations. We can further discuss the ongoing work during our meeting tomorrow.

jverbraeken · 2021-04-28T13:51:03Z

Emphasise on the mobile, lightweight aspect of your solution (e.g., energy consumption and communication costs).

Idea: introduce noisy PSI-CA, but is this good enough?

jverbraeken · 2021-04-29T07:57:14Z

Write for your audience. Motivation can and should be shorter. Given the Middleware audience, you do not need to motivate the need for machine learning, reviewers should know that.

Kinda done

Please thoroughly read and analyse the FLEET paper; I think the experimental evaluation and methodology is very polished.

Kinda done

Emphasise on the mobile, lightweight aspect of your solution (e.g., energy consumption and communication costs).

Re-introduce noise PSI-CA?

Add some exciting (positive) numbers to the last paragraph of your abstract (where you describe the experiments), e.g., Pro-Bristle achieves a X times increase in accuracy, while achieving lower energy consumption compared to existing solutions.

TODO

Highly recommended to add a figure on page 2, visualising your overall architecture and contributions.

Re-introduce noise PSI-CA?

The reviewers that bid on your paper likely are familiar with centralised FL and not with decentralised FL. What makes it different and why do we need to get rid of the centralised server (this is one of the first questions a reviewer might have)?

Done

It seems that the focus is on 1) Byzantine resilience and 2) accuracy in non-iid environments. If they are the main selling points, they can be further elaborated and emphasised.

Kinda done

Position your work accordingly with prior Middleware papers on similar topics. You can already do this in the introduction.

TODO

jverbraeken · 2021-06-07T08:48:32Z

Update June 7th
Thesis (5).pdf

synctext · 2021-06-07T13:08:09Z

Thesis format is unique for our department. It has the paper-inside-thesis format used commonly by pattern recognition research group.
Polish thesis please; have Appendixes an intro sentence? no half empty pages. [148] (2017).. Finally, where is the source code?

jverbraeken · 2021-06-07T13:15:13Z

A second example of a thesis that combines (a) a paper with (b) supplementary material is: https://repository.tudelft.nl/islandora/object/uuid%3A2e793ece-4572-4bb6-83e3-541be467cb4f

synctext · 2021-06-22T09:37:33Z

Review of near-final master thesis. Communicate with Martha on final hand-in or aim for Friday.

Table of contain is unbalanced. Main article has no visibility of main sections. Appendix has too much details.
"In this section, we dis-cuss Bristle’s limitations in detail and identify steps for further research." or in more positive terms: We believe Bistle opens up a fertile new research field, however our work is far from completed and mature. We identitief the following directions for improvement.
Increase the network buffers (rmem_default, rmem_max, wmem_default, and wmem_maxto 80 MB)
Thesis should not contains "shopping lists" like this. Use higher information density, discuss at higher abstraction level and list the 'why it failed to work at scale (e.g. memory constraints with linear growth, cpu, etc.'. Scalability solution copy-on-write Docker sharing https://docs.docker.com/storage/storagedriver/#copying-makes-containers-efficient
Copy and paste from .PDF: "D.1.LI MI TAT I O N S AN D F U T U R E D I R EC T I O N S", re-write section which consists entirely of bullet points.
Presentation requires: "Research Question", "improvement beyond the state-of-the-art"

jverbraeken · 2021-06-23T13:51:41Z

Final thesis version:
Thesis Joost Verbraeken - final version.pdf

synctext · 2021-08-02T08:16:17Z

Thesis completed and in official repository. 🎊 Closing this issue. The source code for federated machine learning is located here:
https://github.com/jverbraeken/trustchain-superapp/commits/master
https://github.com/jverbraeken/kotlin-ipv8

synctext added the type: MSc-Thesis-Work label Mar 18, 2020

synctext added this to the Backlog milestone Mar 18, 2020

synctext assigned jverbraeken Mar 18, 2020

ichorid added the type: project idea label Jul 18, 2020

This was referenced Feb 3, 2021

attack-resilient micro-economy for media #1

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the first AI dAPP - self-evolving AI-DAO #5987

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synctext mentioned this issue Apr 28, 2021

Digital Basic Identity - EU EBSI gateway and "regie op gegevens" #6023

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Thesis: self-sovereign identity (SSI) #5576

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7 tasks

Byzantine-resilient, asynchronous, yet decentralized federated learning #5221

Byzantine-resilient, asynchronous, yet decentralized federated learning #5221

Comments

synctext commented Mar 18, 2020 • edited

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devos50 commented Apr 19, 2020

Atomic Swaps

Auctions

Custodial Exchanges

Decentralized Exchanges

Blockchain-based Energy Trading

Matchmaking

Prediction Markets

Security Aspects of Trading

Settlement Mechanisms

Various

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jverbraeken commented Sep 9, 2020

Must-haves

Step 1

Step 2

Step 3

Should-haves

Step 4

Step 5

Could-haves

Step 6

Step 7

Step 8

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