Communication Graph

Communication graphs model information exchange in distributed systems, focusing on optimizing communication efficiency and robustness for various applications like multi-agent systems and decentralized machine learning. Current research emphasizes developing algorithms and architectures, such as graph neural networks and transformer-based approaches, that dynamically adapt communication patterns based on task demands and data heterogeneity, often incorporating techniques like compression and momentum tracking to improve efficiency. This field is crucial for advancing large-scale machine learning, enabling efficient and resilient distributed computation, and improving the design of complex networked systems.