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: over 3 years ago

Related projects:

RepositoryDescriptionStars
diogenes0319/fedmd_cleanAn implementation of a heterogenous federated learning framework using model distillation.150
gaoliang13/feddcFederated learning algorithm that adapts to non-IID data by decoupling and correcting for local drift81
bytedance/feddecorrImplementation of various federated learning algorithms to mitigate dimensional collapse in heterogeneous federated learning environments64
bdemo/pfedbred_publicA project that proposes a novel federated learning approach to address the issue of incomplete information in personalized machine learning models9
litian96/dittoA framework for personalized federated learning to balance fairness and robustness in decentralized machine learning systems.138
mediabrain-sjtu/fedgelaThis project enables federated learning across partially class-disjoint data with curated bilateral curation.11
hmgxr128/mifa_codeAn implementation of Fast Federated Learning under device unavailability for minimizing latency and achieving optimal convergence rates9
ibm/federated-learning-libA framework for collaborative distributed machine learning in enterprise environments.500
mediabrain-sjtu/feddiscoA federated learning framework with discrepancy-aware collaboration for decentralized data training68
zoesgithub/fedregAn algorithm to improve convergence rates and protect privacy in Federated Learning by addressing the catastrophic forgetting issue during local training26
ignavierng/notears-admmAn implementation of Bayesian network structure learning with continuous optimization for federated learning.10
bibikar/feddstAn implementation of federated learning with sparse training and readjustment mechanisms to reduce communication overhead while maintaining model performance.29
kampmichael/feddcAn implementation of federated daisy-chaining and model averaging for distributed machine learning8
xtra-computing/fedsimA framework that enables federated learning across multiple datasets while optimizing model performance with record similarities.25
lins-lab/fedbrAn implementation of federated learning algorithm to reduce local learning bias and improve convergence on heterogeneous data25