most citedEnhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling

4 citations · 6 across the 2 of their papers we have counts for

collaborators

2 papers

cs.DC2025★ 4 cited

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling

Xiyu Zhao, Qimei Cui, Ziqiang Du +6

Personalized federated learning (PFL) offers a solution to balancing personalization and generalization by conducting federated learning (FL) to guide personalized learning (PL). L…

cs.LG2024★ 2 cited

Cauchy-Schwarz Divergence Information Bottleneck for Regression

Shujian Yu, Xi Yu, Sigurd Løkse +2

The information bottleneck (IB) approach is popular to improve the generalization, robustness and explainability of deep neural networks. Essentially, it aims to find a minimum suf…