Trends and Applications of Distributed Artificial Intelligence (AI) and Associated Systems

Dear Colleagues,

Distributed Artificial Intelligence (DAI) represents a significant paradigm shift in the development of intelligent systems, emphasizing decentralized, collaborative, and autonomous agents that work together to solve complex problems. Unlike traditional centralized AI, where decision-making and data processing occur in a single location or system, DAI distributes these tasks across multiple interconnected nodes or agents, often leading to more scalable, fault-tolerant, and robust AI applications.

Recent advancements in computing technologies, such as the proliferation of edge devices, the growth of the Internet of Things (IoT), and the emergence of blockchain and federated learning, have catalyzed the evolution of DAI systems. These innovations enable distributed AI applications in real-time environments, such as healthcare, finance, robotics, and smart cities, where rapid data processing, real-time decision-making, and privacy-preserving models are crucial.

This Special Issue aims to bring together cutting-edge research on the theoretical foundations, system architectures, and practical implementations of distributed AI. We encourage contributions that explore multi-agent systems, federated learning approaches, and decentralized decision-making mechanisms, with a focus on their applications in large-scale systems and real-world use cases. The exploration of challenges such as communication overhead, security concerns, and coordination among agents will also be a central theme in this Issue, paving the way for future innovations in the field.

Dr. Ron (Rongyu) Lin
Dr. Sihong He
Guest Editors

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