3 papers
cs.LG2024
A Robust Federated Learning Framework for Undependable Devices at Scale
Shilong Wang, Jianchun Liu, Hongli Xu +4
In a federated learning (FL) system, many devices, such as smartphones, are often undependable (e.g., frequently disconnected from WiFi) during training. Existing FL frameworks alw…
cs.DC2024
Many Hands Make Light Work: Accelerating Edge Inference via Multi-Client Collaborative Caching
Wenyi Liang, Jianchun Liu, Hongli Xu +2
Edge inference is a technology that enables real-time data processing and analysis on clients near the data source. To ensure compliance with the Service-Level Objectives (SLOs), s…
cs.DC2024
ParallelSFL: A Novel Split Federated Learning Framework Tackling Heterogeneity Issues
Yunming Liao, Yang Xu, Hongli Xu +3
Mobile devices contribute more than half of the world's web traffic, providing massive and diverse data for powering various federated learning (FL) applications. In order to avoid…