3 accepted papers
Decentralized learning has emerged as a popular method due to its excellent scalability and parallel implementation of stochastic gradient methods. However, the main challenges in decentralized learning include the communication overhead and privacy concerns associated with sharing gradients with ne…
Federated constrained learning allows us to learn a global model with some specific structure to enhance performance. Most existing federated learning techniques assume data is homogeneously or independently and identically distributed (iid) across clients. However, this iid assumption rarely holds…
In this paper, we propose DP-FedFW, a novel Frank-Wolfe based federated learning algorithm with local (ϵ,δ)-differential privacy (DP) guarantees in a constrained learning setting. In DP-FedFW, we perturb local models to ensure privacy while communicating with the server, and each client performs sev…