5 papers
Personalized Federated Learning under Model Dissimilarity Constraints
Samuel Erickson, Mikael Johansson
One of the defining challenges in federated learning is that of statistical heterogeneity among clients. We address this problem with KARULA, a regularized strategy for personalize…
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…
Temporal Predictive Coding for Gradient Compression in Distributed Learning
Adrian Edin, Zheng Chen, Michel Kieffer +1
This paper proposes a prediction-based gradient compression method for distributed learning with event-triggered communication. Our goal is to reduce the amount of information tran…