With its privacy-preserving and decentralized features, distributed learning plays an
irreplaceable role in the era of wireless networks with a plethora of smart terminals, an …

Distributed intelligence is a well-known approach for optimizing interactions among
numerous smart devices that interconnect and operate together as Internet of Things (IoT) …

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 …

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 …

Fairness and robustness are two important goals in the design of modern distributed
learning systems. Despite a few prior works attempting to achieve both fairness and …

Motivated by the emerging federated learning applications, we jointly consider the problems
of Byzantine-robustness and communication efficiency in distributed non-convex learning …

To address the communication bottleneck problem in distributed optimization within a
master-worker framework, we propose LocalNewton, a distributed second-order algorithm …

The emerging federated learning applications raise challenges of Byzantine-robustness and
communication efficiency in distributed non-convex learning over non-IID data. To address …

To address the communication bottleneck problem in distributed optimization within a
master-worker framework, we propose LocalNewton, a distributed second-order algorithm …

Federated learning is a decentralized machine learning framework that enables
collaborative model training without revealing raw data. Due to the diverse hardware and …