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arXiv:2608. 15639v1 Announce Type: cross Abstract: \textit{Split Federated Learning} (SFL) enables distributed model training by splitting networks between the server and clients.
The paper addresses the mismatch between learner and client data distributions in federated learning, noting that traditional client selection methods often ignore this misalignment. It introduces a dynamic, influence-aware client selection framework that uses a small proxy dataset to estimate each client's utility for the learner’s objective, prioritizing informative sources while mitigating noise and heterogeneity. Experiments on CIFAR-10 with heterogeneous partitions show the proposed method outperforms static and dynamic baselines, achieving faster convergence and higher accuracy.
Optimal Hydra is a transformer‑based heterogeneous federated learning framework designed for fine‑tuning large language models. It creates client‑specific sub‑networks, called Hydra‑nets, by selecting global layers and tailoring attention heads to each client’s local data distribution. Experiments on three datasets demonstrate significant performance gains over baseline methods while reducing local resource demands.
arXiv:2608. 12108v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training across distributed clients while keeping data local.
FedRAPP introduces a personalized aggregation framework for federated test‑time prompt tuning of vision‑language models, enabling distributed clients to adapt soft prompts on unlabeled test streams without source data. The method incorporates reliability‑aware client assessment, server‑side exponential moving‑average prompt memory, and similarity‑guided peer aggregation to selectively combine information from reliable and compatible clients. Experiments on nine CLIP benchmark datasets and ImageNet variants show that FedRAPP consistently outperforms standard federated aggregation baselines under heterogeneous client distributions while preserving prompt‑only adaptation efficiency.
Asynchronous federated learning improves scalability by updating the global model from a server-side buffer of client updates as they arrive, rather than waiting for all selected clients to finish. Wh...