Multi-agent systems can solve scientific issues related to complex systems that are difficult or
impossible for a single agent to solve through mutual collaboration and cooperation …

Drawing on the theory of planned behavior (TPB) and innovation diffusion theory (IDT), this
study aims to reveal the mechanism of how personal innovativeness in the domain of …

This paper studies distributed online learning under Byzantine attacks. The performance of
an online learning algorithm is often characterized by (adversarial) regret, which evaluates …

In online decision making, feedback delays often arise due to the latency caused by
computation and communication in practical systems. In this article, we study decentralized …

Distributed online learning is gaining increased traction due to its unique ability to process
large-scale datasets and streaming data. To address the growing public awareness and …

In this paper, the distributed online optimisation problem is considered in an unknown
dynamic environment. Compared with the existing results, an unknown dynamic …

We present a distributed optimization algorithm for solving online personalized optimization
problems over a network of computing and communicating nodes, each of which linked to a …

In this article, we propose a model-based approach to the design of online optimization
algorithms, with the goal of improving the tracking of the solution trajectory (trajectories) wrt …

Motivated by the real-world application of traffic classification at the network edge, we study
the problem of robust decentralized online learning against malicious data generators that …

The need for fast sparse optimization is emerging, eg, to deal with large-dimensional data-
driven problems and to track time-varying systems. In the framework of linear sparse …