In this paper, we investigate the problem of multi-dimensional consensus subject to the
internal agent dynamics constraint and external non-cooperative malicious attacks. We …

Distributed machine learning algorithms are increasingly used in multirobot systems and are
prone to Byzantine attacks. In this article, we consider a distributed implementation of the …

In this paper, we study the resilient vector consensus problem in networks with adversarial
agents and improve resilience guarantees of existing algorithms. A common approach to …

We consider the Byzantine-robust decentralized stochastic optimization problem, where
every agent periodically communicates with its neighbors to exchange the local models, and …

This paper studies Byzantine-robust stochastic optimization over a decentralized network,
where every agent periodically communicates with its neighbors to exchange local models …

Meta-learning is a learning-to-learn paradigm that leverages past learning experiences for
quick adaptation to new learning tasks. It has a wide application, such as in few-shot …

Distributed multi-task learning provides significant advantages in multi-agent networks with
heterogeneous data sources where agents aim to learn distinct but correlated models …

This article highlights the importance of learning models which prevent the resilient attack of
robotic technology with a subset of trajectories. Many complement models are introduced in …

This paper studies Byzantine-robust stochastic optimization over a decentralized network,
where every agent periodically communicates with its neighbors to exchange local models …

Federated learning is a privacy-preserving machine learning paradigm to protect the data of
clients against privacy breaches. Federated learning algorithms are further reinforced with …