Communication Constraint

Communication constraint in distributed systems focuses on optimizing information exchange to improve efficiency and performance while adhering to bandwidth limitations. Current research emphasizes developing algorithms and model architectures (e.g., graph convolutional networks, adaptive compression techniques) for efficient data transmission and aggregation in various applications, including federated learning and multi-agent systems. This research area is crucial for advancing distributed machine learning and robotics, enabling the development of scalable and resource-efficient systems in resource-constrained environments. The ultimate goal is to achieve optimal trade-offs between communication overhead and performance metrics like accuracy and convergence speed.