Randomized Communication

Randomized communication in decentralized learning aims to improve the efficiency and robustness of distributed machine learning systems by strategically managing inter-node communication. Current research focuses on developing algorithms that leverage randomized communication patterns, such as probabilistic message passing and gossip protocols, to achieve faster convergence and reduced communication overhead, even in unreliable or resource-constrained environments. These advancements are significant for enabling scalable and privacy-preserving machine learning on large-scale networks, particularly in applications like the Internet of Things (IoT) and federated learning. Improved efficiency and robustness are achieved through techniques like sparsification of model parameters and non-coherent over-the-air communication.