Decentralized Communication

Decentralized communication focuses on enabling efficient information exchange among multiple agents or nodes without relying on a central server, aiming to improve robustness, privacy, and scalability. Current research emphasizes developing algorithms and models, such as those based on graph neural networks and dual-level recurrent frameworks, to optimize communication efficiency and address challenges like adversarial attacks and model inconsistency in applications such as federated learning. These advancements are significant for improving the performance and resilience of distributed systems across various fields, including machine learning and multi-agent systems.