7 accepted papers
This paper explores locally differentially private distributed algorithms that solve non-convex empirical risk minimization problems. Traditional approaches often assume uniformly bounded stochastic gradients, which may not hold in practice. To address this issue, we propose differentially **Pri**…
Decentralized training of deep neural networks has attracted significant attention for its theoretically superior scalability compared to synchronous data-parallel methods like All-Reduce. However, realizing this potential in multi-node training is challenging due to the complex design space that in…
We propose a novel algorithm for solving the composite Federated Learning (FL) problem. This algorithm manages non-smooth regularization by strategically decoupling the proximal operator and communication, and addresses client drift without any assumptions about data similarity. Moreover, each worke…
This paper proposes a locally differentially private federated learning algorithm for strongly convex but possibly nonsmooth problems that protects the gradients of each worker against an honest but curious server. The proposed algorithm adds artificial noise to the shared information to ensure priv…
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 work studying the design and analysis of algorithms for manifold optimization in the centralized setting, there are current…
This paper investigates personalized federated learning, in which a group of workers are coordinated by a server to train correlated local models, in addition to a common global model. This distributed statistical learning problem faces two challenges: efficiency of information exchange between the…
Existing self-supervised learning methods based on contrastive learning and masked image modeling have demonstrated impressive performances. However, current masked image modeling methods are mainly utilized in natural images, and their applications in medical images are relatively lacking. Besides,…