4 papers
Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimization
Jake B. Perazzone, Shiqiang Wang, Mingyue Ji +1
Federated learning (FL) is a useful tool that enables the training of machine learning models over distributed data without having to collect data centrally. When deploying FL in c…
Optimal Communication and Key Rate Region for Hierarchical Secure Aggregation with User Collusion
Xiang Zhang, Kai Wan, Hua Sun +3
Secure aggregation is concerned with the task of securely uploading the inputs of multiple users to an aggregation server without letting the server know the inputs beyond their su…
A New Theoretical Perspective on Data Heterogeneity in Federated Optimization
Jiayi Wang, Shiqiang Wang, Rong-Rong Chen +1
In federated learning (FL), data heterogeneity is the main reason that existing theoretical analyses are pessimistic about the convergence rate. In particular, for many FL algorith…
A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging
Shiqiang Wang, Mingyue Ji
In federated learning (FL), clients usually have diverse participation statistics that are unknown a priori, which can significantly harm the performance of FL if not handled prope…