GradMA

Federated learning accelerator

A framework for accelerating federated learning with memory-based acceleration and alleviation of catastrophic forgetting

GradMA: A Gradient-Memory-based Accelerated Federated Learning with Alleviated Catastrophic Forgetting

GitHub

13 stars
1 watching
4 forks
Language: Python
last commit: almost 2 years ago

Related projects:

Repository Description Stars
diogenes0319/fedmd_clean An implementation of a heterogenous federated learning framework using model distillation. 150
gaoliang13/feddc Federated learning algorithm that adapts to non-IID data by decoupling and correcting for local drift 81
bytedance/feddecorr Implementation of various federated learning algorithms to mitigate dimensional collapse in heterogeneous federated learning environments 64
bdemo/pfedbred_public A project that proposes a novel federated learning approach to address the issue of incomplete information in personalized machine learning models 9
litian96/ditto A framework for personalized federated learning to balance fairness and robustness in decentralized machine learning systems. 138
mediabrain-sjtu/fedgela This project enables federated learning across partially class-disjoint data with curated bilateral curation. 11
hmgxr128/mifa_code An implementation of Fast Federated Learning under device unavailability for minimizing latency and achieving optimal convergence rates 9
ibm/federated-learning-lib A framework for collaborative distributed machine learning in enterprise environments. 500
mediabrain-sjtu/feddisco A federated learning framework with discrepancy-aware collaboration for decentralized data training 68
zoesgithub/fedreg An algorithm to improve convergence rates and protect privacy in Federated Learning by addressing the catastrophic forgetting issue during local training 26
ignavierng/notears-admm An implementation of Bayesian network structure learning with continuous optimization for federated learning. 10
bibikar/feddst An implementation of federated learning with sparse training and readjustment mechanisms to reduce communication overhead while maintaining model performance. 29
kampmichael/feddc An implementation of federated daisy-chaining and model averaging for distributed machine learning 8
xtra-computing/fedsim A framework that enables federated learning across multiple datasets while optimizing model performance with record similarities. 25
lins-lab/fedbr An implementation of federated learning algorithm to reduce local learning bias and improve convergence on heterogeneous data 25