Found by comparing what the projects do, not just their names.
Federated Learning Framework
An implementation of a robust federated learning framework for handling noisy and heterogeneous clients in machine learning.
Federated Learning Framework
A framework for tackling heterogeneity and catastrophic forgetting in federated learning by leveraging cross-correlation and similarity learning
FL framework
Enables the training and validation of machine learning models on distributed datasets in a secure and scalable manner.
Federated Learning Framework
A framework for personalized federated learning to balance fairness and robustness in decentralized machine learning systems.
Federated Learning Framework
An implementation of a personalized federated learning framework with decentralized sparse training and peer-to-peer communication protocol.
Federated Learning Framework
This project presents a framework for robust federated learning against backdoor attacks.
Federated Learning Framework
Develops a framework to address label skews in one-shot federated learning by partitioning data and adapting models.
Federated Learning Framework
A flexible framework for distributed machine learning where participants train local models and collaboratively optimize them without sharing data
FedFR framework
An open-source software framework for jointly optimizing face recognition models in federated learning settings.
Federated Active Learning Framework
An implementation of federated active learning with a novel sampling strategy to improve performance on decentralized machine learning tasks
Federated Learning Framework
Implementation of a communication-efficient federated learning framework using sparse random neural networks.
Federated ML framework
An implementation of federated multi-task learning with laplacian regularization across various datasets
Shift robust FL framework
A framework for personalized federated learning that improves shift-robustness with minimal extra training overhead
Federated learning framework
Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions.
Federated Learning Framework
Evaluates various methods for federated learning on different models and tasks.