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cs.DC2025
Federated Fine-Tuning of Sparsely-Activated Large Language Models on Resource-Constrained Devices
Fahao Chen, Jie Wan, Peng Li +2
Federated fine-tuning of Mixture-of-Experts (MoE)-based large language models (LLMs) is challenging due to their massive computational requirements and the resource constraints of…
cs.DC2024★ 22 cited
FedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Quality
Biwei Yan, Hongliang Zhang, Minghui Xu +2
Federated learning is a powerful technique that enables collaborative learning among different clients. Prototype-based federated learning is a specific approach that improves the…