Federated Learning Utilities and Tools for Experimentation
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Updated
Jan 11, 2024 - Python
Federated Learning Utilities and Tools for Experimentation
(NeurIPS 2022) Official Implementation of "Preservation of the Global Knowledge by Not-True Distillation in Federated Learning"
Docker CLI package for the vantage6 infrastructure
Federated Neural Collaborative Filtering (FedNCF). Neural Collaborative Filtering utilizes the flexibility, complexity, and non-linearity of Neural Network to build a recommender system. Aim to federate this recommendation system.
A Comprehensive and Versatile Open-Source Federated Learning Framework
A flexible, modular, and easy to use library to facilitate federated learning research and development in healthcare settings
Auto-Multilift is a novel learning framework for cooperative load transportation with quadrotors. It can automatically tune various MPC hyperparameters, which are modeled by DNNs and difficult to tune manually, via reinforcement learning in a distributed and closed-loop manner.
A project for simulation of Asynchronous Federated Learning
Sageflow: Robust Federated Learning against Both Stragglers and Adversaries at NeurIPS'21
Implementation of the FedPM framework by the authors of the ICLR 2023 paper "Sparse Random Networks for Communication-Efficient Federated Learning".
Unofficial implementation of SignSGD to assess its robustness to adversaries.
Official implementation of Deep Class Incremental Learning from Decentralized Data (IEEE TNNLS 22)
This is a distributed training framework for continual and incremental learning for multi-label multi-class image tasks
FedStream: Prototype-Based Federated Learning on Distributed Concept-drifting Data Streams
Papers related to Federated Learning in all venue (dblp)
Reinforcement learning using PPO on the 3D Ball environment for Unity ML-Agents. Using MPI to do distributed training across separate processes
[WIP] elastic training implemented with MXNet
This repository is the code basis of the paper intitled "The learning costs of Federated Learning in constrained scenarios"
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