3 papers
cs.LG2025
Non-convex composite federated learning with heterogeneous data
Jiaojiao Zhang, Jiang Hu, Mikael Johansson
We propose an innovative algorithm for non-convex composite federated learning that decouples the proximal operator evaluation and the communication between server and clients. Mor…
cs.LG2024
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data
Jiaojiao Zhang, Jiang Hu, Anthony Man-Cho So +1
Many machine learning tasks, such as principal component analysis and low-rank matrix completion, give rise to manifold optimization problems. Although there is a large body of wor…
cs.LG2023
Composite federated learning with heterogeneous data
Jiaojiao Zhang, Jiang Hu, Mikael Johansson
We propose a novel algorithm for solving the composite Federated Learning (FL) problem. This algorithm manages non-smooth regularization by strategically decoupling the proximal op…