Computation Communication

Computation communication research focuses on optimizing the interplay between computational processing and data transmission, aiming to minimize latency and resource consumption in distributed systems. Current efforts concentrate on developing efficient algorithms and architectures, such as those leveraging gradient tracking, hypergame theory, and model-agnostic meta-learning, to manage the trade-off between computation and communication in various applications, including federated learning and large language model inference. This field is crucial for advancing the performance and scalability of AI and machine learning, particularly in resource-constrained environments like edge computing and wireless networks, impacting both theoretical understanding and practical deployment of these technologies.