74 citations · 104 across the 8 of their papers we have counts for
4 papers · 1 filter
Mode Connectivity and Data Heterogeneity of Federated Learning
Tailin Zhou, Jun Zhang, Danny H. K. Tsang
Federated learning (FL) enables multiple clients to train a model while keeping their data private collaboratively. Previous studies have shown that data heterogeneity between clie…
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency
Jiawei Shao, Zijian Li, Wenqiang Sun +6
Federated learning (FL) has emerged as a secure paradigm for collaborative training among clients. Without data centralization, FL allows clients to share local information in a pr…
Attention-based QoE-aware Digital Twin Empowered Edge Computing for Immersive Virtual Reality
Jiadong Yu, Ahmad Alhilal, Tailin Zhou +2
Metaverse applications such as virtual reality (VR) content streaming, require optimal resource allocation strategies for mobile edge computing (MEC) to ensure a high-quality user…
Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Data
Tailin Zhou, Zehong Lin, Jun Zhang +1
Model averaging is a widely adopted technique in federated learning (FL) that aggregates multiple client models to obtain a global model. Remarkably, model averaging in FL yields a…