TL;DR
A federated learning framework based on self-excluding aggregation (FedSEA) is proposed by integrating dynamic sparsity-driven client grouping, self-excluding aggregation and cross-group knowledge distillation, which achieves superior performance on the Office-Home and CIFAR-10 datasets.
PDKD solves the problem of model drift caused by the inconsistent distribution of distillation datasets and the local data by generating personalized distillation datasets for each client while protecting client data privacy.
Federated learning (FL) is an emerging distributed machine learning framework that enables collaborative learning among multiple parties while preserving data privacy. However, the complexity of environments and node heterogeneity in the real world result in uneven data distribution across nodes, leading to Non-IID (No...
A domain-aware proxy selection framework to better adopt proxy data for OOD problems is proposed and the experimental results show that the proposed models effectively address the challenges of distribution shifts under OOD with and without proxy data.
This study implemented a comprehensive experimental framework for analysing FL performance using standard FL aggregation protocols FedAvg, FedProx, and SCAFFOLD in conjunction with Differential Privacy mechanisms; specifically, the Gaussian noise mechanism with Rényi Differential Privacy (RDP) accountants.
Federated learning (FL) is a practical framework that can train models on distributed user data while guaranteeing data privacy; however, due to heterogeneity in which each user has a different data distribution, problems frequently arise where both global and personalization performance deteriorate simultaneously. Thi...
Federated learning is a decentralised machine-learning approach in which several clients jointly build a shared model without moving their raw data to one location. Rising concerns around privacy, tightening regulation, and restrictions on how data may be owned or shared have made this approach increasingly attractive...
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