3 papers
cs.CR2025
LiteChain: A Lightweight Blockchain for Verifiable and Scalable Federated Learning in Massive Edge Networks
Handi Chen, Rui Zhou, Yun-Hin Chan +3
Leveraging blockchain in Federated Learning (FL) emerges as a new paradigm for secure collaborative learning on Massive Edge Networks (MENs). As the scale of MENs increases, it bec…
cs.LG2024
Synergizing Foundation Models and Federated Learning: A Survey
Shenghui Li, Fanghua Ye, Meng Fang +4
Over the past few years, the landscape of Artificial Intelligence (AI) has been reshaped by the emergence of Foundation Models (FMs). Pre-trained on massive datasets, these models…
cs.LG2023
Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning
Yun-Hin Chan, Rui Zhou, Running Zhao +2
Federated learning (FL) inevitably confronts the challenge of system heterogeneity in practical scenarios. To enhance the capabilities of most model-homogeneous FL methods in handl…