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20242026
most citedFedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection

2 citations · 3 across the 10 of their papers we have counts for

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cs.CL2026

EdgeFlowerTune: Evaluating Federated LLM Fine-Tuning Under Realistic Edge System Constraints

Jiaxiang Geng, Yiyi Lu, Lunyu Zhao +3

Federated fine-tuning offers a promising paradigm for adapting large language models (LLMs) on edge devices by leveraging the rich, diverse, and continuously generated data from sm…