Distributed Machine Learning Patterns from Manning Publications by Yuan Tang https://bit.ly/2RKv8Zo
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Updated
Aug 11, 2024 - Python
Distributed Machine Learning Patterns from Manning Publications by Yuan Tang https://bit.ly/2RKv8Zo
This is suite of the hands-on training materials that shows how to scale CV, NLP, time-series forecasting workloads with Ray.
[ICLR 2021] HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients
FedERA is a modular and fully customizable open-source FL framework, aiming to address these issues by offering comprehensive support for heterogeneous edge devices and incorporating both standalone and distributed computing. It includes new software modules to enhance usability and promote environ- mental sustainability.
GeoMX: A fast and unified system for distributed machine learning over geo-distributed data centers.
Paddle with Decentralized Trust based on Xuperchain
A curated list of Federated Learning papers/articles and recent advancements.
sensAI: ConvNets Decomposition via Class Parallelism for Fast Inference on Live Data
Distributed Bayesian Entity Resolution in Apache Spark
🔨 A toolbox for federated learning, aiming to provide implementations of FedAvg, FedProx, Ditto, etc. in multiple versions, such as Pytorch/Tensorflow, single-machine/distributed, synchronized/asynchronous.
CSCE 585 - Machine Learning Systems
Nerlnet is a framework for research and development of distributed machine learning models on IoT
vector quantization for stochastic gradient descent.
Python module for simulating gossip learning.
[NeurIPS 2022] SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training
The code of AAAI-21 paper titled "Defending against Backdoors in Federated Learning with Robust Learning Rate".
🔨 使用Spark/Pytorch实现分布式算法,包括图/矩阵计算(graph/matrix computation)、随机算法、优化(optimization)和机器学习。参考刘铁岩《分布式机器学习》和CME 323课程
Atomo: Communication-efficient Learning via Atomic Sparsification
Materials for "Machine Learning on Big Data" course
Event-Triggered Communication in Parallel Machine Learning
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