arXiv Machine Learning
Aug 31

Beyond Non-IID: Learner--Client Distribution Mismatch in Federated Learning

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.

By Yiming Xie, Lili Su, Ningfang Mi
arXiv AI
Oct 9

Task-Centric Personalized Federated Fine-Tuning of Language Models

The paper introduces FedRouter, a personalized federated learning framework that clusters tasks rather than clients to build specialized language models. It employs local and global clustering of adapters to associate them with specific tasks and includes an evaluation router that directs test samples to the most suitable adapter. Experiments on a multitask dataset show FedRouter outperforms existing methods, achieving up to 6.1% relative improvement under task interference and 136% under generalization scenarios.

By Gabriel U. Talasso, Meghdad Kurmanji, Allan M. de Souza, Nicholas D. Lane, Leandro A. Villas