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

Distributed machine learning (DML) techniques, such as federated learning, partitioned
learning, and distributed reinforcement learning, have been increasingly applied to wireless …

Byzantine-robust federated learning aims to enable a service provider to learn an accurate
global model when a bounded number of clients are malicious. The key idea of existing …

Decentralized stochastic gradient descent (SGD) is a driving engine for decentralized
federated learning (DFL). The performance of decentralized SGD is jointly influenced by …

Machine learning has begun to play a central role in many applications. A multitude of these
applications typically also involve datasets that are distributed across multiple computing …

Byzantine-robustness has been gaining a lot of attention due to the growth of the interest in
collaborative and federated learning. However, many fruitful directions, such as the usage of …

As the scale of distributed control systems over networks increases and interactions
between different subsystems become more sophisticated, questions of the resilience of …

We consider the problem of multiagent optimization wherein an unknown subset of agents
suffer Byzantine faults and thus behave adversarially. We assume that each agent i has a …

Distributed learning has become a critical enabler of the massively connected world that
many people envision. This article discusses four key elements of scalable distributed …

Due to the implementation and performance limitations of centralized learning automatic
modulation classification (CentAMC) method, this paper proposes a decentralized learning …