8 papers
An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data
Jiaojiao Zhang, Yuqi Xu, Yizhao Fan +1
This work addresses the key challenges of applying federated learning to large-scale deep neural networks, particularly the issue of client drift due to data heterogeneity across c…
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…
Locally Differentially Private Online Federated Learning With Correlated Noise
Jiaojiao Zhang, Linglingzhi Zhu, Dominik Fay +1
We introduce a locally differentially private (LDP) algorithm for online federated learning that employs temporally correlated noise to improve utility while preserving privacy. To…
From promise to practice: realizing high-performance decentralized training
Zesen Wang, Jiaojiao Zhang, Xuyang Wu +1
Decentralized training of deep neural networks has attracted significant attention for its theoretically superior scalability over synchronous data-parallel methods like All-Reduce…
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…
Soft quasi-Newton: Guaranteed positive definiteness by relaxing the secant constraint
Erik Berglund, Jiaojiao Zhang, Mikael Johansson
We propose a novel algorithm, termed soft quasi-Newton (soft QN), for optimization in the presence of bounded noise. Traditional quasi-Newton algorithms are vulnerable to such pert…
Differentially Private Online Federated Learning with Correlated Noise
Jiaojiao Zhang, Linglingzhi Zhu, Mikael Johansson
We introduce a novel differentially private algorithm for online federated learning that employs temporally correlated noise to enhance utility while ensuring privacy of continuous…
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…