sparse-random-networks
Federated Learning Framework
Implementation of a communication-efficient federated learning framework using sparse random neural networks.
Implementation of the FedPM framework by the authors of the ICLR 2023 paper "Sparse Random Networks for Communication-Efficient Federated Learning".
29 stars
2 watching
2 forks
Language: Python
last commit: almost 2 years ago compressiondeep-learningdistributed-learningfederated-learningpruningsparse-network
Related projects:
Repository | Description | Stars |
---|---|---|
bibikar/feddst | An implementation of federated learning with sparse training and readjustment mechanisms to reduce communication overhead while maintaining model performance. | 29 |
omarfoq/fedem | Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions. | 154 |
mediabrain-sjtu/fedgela | Federated learning algorithm designed to handle partially class-disjoint data by utilizing bilateral curation and Dirichlet partitioning. | 10 |
mediabrain-sjtu/pfedgraph | This project enables personalized federated learning with inferred collaboration graphs to improve the performance of machine learning models on non-IID (non-independent and identically distributed) datasets. | 26 |
pengyang7881187/fedrl | Enabling multiple agents to learn from heterogeneous environments without sharing their knowledge or data | 54 |
harliwu/fedamd | This project presents an approach to federated learning with partial client participation by optimizing anchor selection for improving model accuracy and convergence. | 2 |
flint-xf-fan/byzantine-federated-rl | Provides a framework and theoretical foundation for Federated Reinforcement Learning with Byzantine Resilience in distributed systems | 85 |
xtra-computing/fedov | Develops a framework to address label skews in one-shot federated learning by partitioning data and adapting models. | 14 |
ignavierng/notears-admm | An implementation of Bayesian network structure learning with continuous optimization for federated learning. | 10 |
wenkehuang/fccl | A framework for tackling heterogeneity and catastrophic forgetting in federated learning by leveraging cross-correlation and similarity learning | 97 |
lyn1874/fedpvr | An implementation of a federated learning algorithm for handling heterogeneous data | 6 |
hongliny/fedac-neurips20 | Provides code for a federated learning algorithm to optimize machine learning models in a distributed setting. | 14 |
jinheonbaek/fed-pub | Personalized Subgraph Federated Learning framework for distributed machine learning | 44 |
rong-dai/dispfl | An implementation of a personalized federated learning framework with decentralized sparse training and peer-to-peer communication protocol. | 68 |
omarfoq/knn-per | A federated learning framework with personalized memorization using deep neural networks and k-nearest neighbors for collaborative learning of statistical models | 42 |