3 papers
cs.DC2025
Robust Federated Fine-Tuning in Heterogeneous Networks with Unreliable Connections: An Aggregation View
Yanmeng Wang, Zhiwen Dai, Shuai Wang +4
Federated Fine-Tuning (FFT) has attracted growing interest as it leverages both server- and client-side data to enhance global model generalization while preserving privacy, and si…
cs.DC2025
Robust Federated Learning in Unreliable Wireless Networks: A Client Selection Approach
Yanmeng Wang, Wenkai Ji, Jian Zhou +2
Federated learning (FL) has emerged as a promising distributed learning paradigm for training deep neural networks (DNNs) at the wireless edge, but its performance can be severely…
eess.SY2025
Lightweight Federated Learning in Mobile Edge Computing with Statistical and Device Heterogeneity Awareness
Jinghong Tan, Zhichen Zhang, Kun Guo +2
Federated learning enables collaborative machine learning while preserving data privacy, but high communication and computation costs, exacerbated by statistical and device heterog…