The problem of Byzantine resilience in distributed machine learning, aka Byzantine machine
learning, consists of designing distributed algorithms that can train an accurate model …

Due to its decentralized nature, Federated Learning (FL) lends itself to adversarial attacks in
the form of backdoors during training. The goal of a backdoor is to corrupt the performance …

In this paper, we develop distributed optimization algorithms that are provably robust against
Byzantine failures—arbitrary and potentially adversarial behavior, in distributed computing …

We study the resilience to Byzantine failures of distributed implementations of Stochastic
Gradient Descent (SGD). So far, distributed machine learning frameworks have largely …

We consider the problem of distributed learning, where a network of agents collectively aim
to agree on a hypothesis that best explains a set of distributed observations of conditionally …

We study robust distributed learning that involves minimizing a non-convex loss function
with saddle points. We consider the Byzantine setting where some worker machines have …

We overview some results on distributed learning with focus on a family of recently proposed
algorithms known as non-Bayesian social learning. We consider different approaches to the …

This paper addresses the multi-armed bandit problem in a multi-player framework. Players
explore a finite set of arms with stochastic rewards, and the reward distribution of each arm …

This paper considers the problem of Byzantine fault-tolerance in distributed multi-agent
optimization. In this problem, each agent has a local cost function. The goal of a distributed …

This article considers resilient cooperative state estimation in unreliable multiagent
networks. A network of agents aim to collaboratively estimate the value of an unknown …