arXiv:2606. 26037v1 Announce Type: cross Abstract: Federated learning has emerged as the foremost approach for decentralized model training with privacy preservation.
By Guangzheng Hu, Patricia Men\'endez, Feng Liu, Mingming Gong, Guanghui Wang, Liuhua Peng
arXiv:2608. 09221v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training across distributed client devices while preserving data privacy.
By Radwan Selo, Majid Kundroo, Taehong Kim
Federated Learning (FL) enables collaborative model training across distributed client devices while preserving data privacy. However, FL faces significant challenges due to data heterogeneity, particularly in terms of label distribution skewness and variations in dataset sizes, which can lead to biased model updates and hinder convergence.
arXiv:2606. 31742v1 Announce Type: cross Abstract: Explainable AI (XAI) methods have demonstrated significant success in recent years at identifying relevant features in input data that drive deep learning model decisions, enhancing interpretability for users.
By Maximilian Andreas Hoefler, Karsten Mueller, Wojciech Samek
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
Federated Learning (FL) enables distributed clients to collaboratively train models without sharing raw data, making it promising for leveraging massive devices in communication networks. In distillat...