arXiv Machine Learning
Communication-Efficient Clustered Federated Learning in Non-IID Wireless Networks
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StoCFL: A Stochastically Clustered Federated Learning Framework for Non-IID Data with Dynamic Client Participation
StoCFL is a clustered federated learning framework designed to address Non-IID data and dynamic client participation. It introduces a flexible clustering mechanism that allows arbitrary client participation and accommodates newly joined clients, improving data efficiency and model performance. Experiments on four Non-IID settings and a real-world dataset demonstrate that StoCFL achieves promising cluster results even when the number of clusters is unknown, outperforming baseline approaches across various scenarios.
arXiv AI
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FedLBW: A Loss-Based Weighting Strategy for Federated Learning on Non-IID Data in Wireless Networks
arXiv:2608. 07007v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative machine learning (ML) across distributed clients while preserving privacy.
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arXiv Machine Learning
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Unified Robust and Convergent Over-The-Air Federated Learning under Channel Noise and Heterogeneity
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arXiv Machine Learning
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Towards Serverless Semi-Decentralized Federated Learning with Heterogeneous Optimizers
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Hugging Face Trending Papers
Aug 10
FedA2L: Adaptive layer-wise learning rate adjustment in decentralized federated learning
Decentralized intelligence systems with heterogeneous devices and limited coordination increasingly rely on decentralized federated learning (DFL). However, DFL suffers from convergence inefficiency under data heterogeneity due to the use of a uniform learning rate (LR) that ignores layer-specific optimization needs.