We address the problem of Federated Learning (FL) where users are distributed and
partitioned into clusters. This setup captures settings where different groups of users have …

Federated learning (FL) is a promising technique for resolving the rising privacy and security
concerns. Its main ingredient is to cooperatively learn the model among the distributed …

We address the problem of federated learning (FL) where users are distributed and
partitioned into clusters. This setup captures settings where different groups of users have …

We study a federated variant of the best-arm identification problem in stochastic multi-armed
bandits: a set of clients, each of whom can sample only a subset of the arms, collaborate via …

Distributed learning has emerged as a leading paradigm for training large machine learning
models. However, in real-world scenarios, participants may be unreliable or malicious …

Byzantine robustness is an essential feature of algorithms for certain distributed optimization
problems, typically encountered in collaborative/federated learning. These problems are …

Despite their high computation and communication costs, Newton-type methods remain an
appealing option for distributed training due to their robustness against ill-conditioned …

We consider a linear stochastic bandit problem involving $ M $ agents that can collaborate
via a central server to minimize regret. A fraction $\alpha $ of these agents are adversarial …