Communication Round

Communication rounds in distributed machine learning and multi-agent systems are a critical area of research focused on minimizing communication overhead while maintaining model accuracy and efficiency. Current efforts explore techniques like demand-aware communication protocols, multi-round cohort interactions, and generative AI for semantic communication to reduce the number of rounds needed for convergence. These advancements are significant because they directly impact the scalability, energy efficiency, and privacy preservation of federated learning and other distributed applications, particularly in resource-constrained environments. Improved communication efficiency translates to faster training times and reduced costs in various domains, including smart environments and mobile networks.