arXiv AI
HO-FL: Hybrid-Order Federated Learning for Heterogeneous Edge Devices
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Efficient Zeroth-Order Federated Finetuning of Language Models on Resource-Constrained Devices
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Convergence Analysis of Sequential Federated Learning on Heterogeneous Data
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GAPSL: A Gradient-Aligned Parallel Split Learning over Data-Heterogeneous Edge Computing Systems
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Ampere: Communication-Efficient and High-Accuracy Split Federated Learning
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arXiv Machine Learning
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Communication-Efficient Federated Learning under Dynamic Device Arrival and Departure: Convergence Analysis and Algorithm Design
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Collate: Collaborative Neural Network Learning for Latency-Critical Edge Systems
arXiv:2607. 08013v1 Announce Type: new Abstract: Federated Learning (FL) empowers multiple clients to collaboratively learn a model, enlarging the training data of each client for high accuracy while protecting data privacy.