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

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 …

This article focuses on decentralized stochastic optimization in the presence of Byzantine
attacks. During the optimization process, an unknown number of malfunctioning or malicious …

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

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty
machines that may deviate from their prescribed algorithm because of software or hardware …

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 …

The infrastructure necessary for training state-of-the-art models is becoming overly
expensive, which makes training such models affordable only to large corporations and …

In this article, we study secure distributed optimization against arbitrary gradient attacks in
multi-agent networks. In distributed optimization, there is no central server to coordinate …

We present a partially personalized formulation of Federated Learning (FL) that strikes a
balance between the flexibility of personalization and cooperativeness of global training. In …

Robustness to Byzantine attacks is a necessity for various distributed training scenarios.
When the training reduces to the process of solving a minimization problem, Byzantine …