A new robust continuous-time optimization algorithm for distributed problems is presented
which guarantees fixed-time convergence. The algorithm is based on a Lyapunov function …

Federated learning (FL) is an emerging machine learning paradigm, in which clients jointly
learn a model with the help of a cloud server. A fundamental challenge of FL is that the …

In this paper, the problem of online distributed optimization with stochastic and nonconvex
objective functions is studied by employing a multi-agent system. When making decisions …

Federated learning facilitates collaborative learning among multiple parties while ensuring
client privacy. The vulnerability of federated learning to diverse Byzantine attacks stems from …

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 …

In this paper, the problem of online distributed optimization with coupled inequality
constraints is studied by employing multi-agent systems. Each agent only has access to the …

In practical federated learning (FL) systems, the presence of malicious Byzantine attacks
and data heterogeneity often introduces biases into the learning process. However, existing …

Active client selection (ACS) strategically identifies clients for model updates during each
training round of federated learning. In scenarios with limited communication resources …

Distributed machine learning enables scalability and computational offloading, but requires
significant levels of communication. Consequently, communication efficiency in distributed …

Existing algorithms in online optimization usually rely on trustful information, eg, reliable
knowledge of gradients, which makes them vulnerable to attacks. To take into account the …