Local Consensus

Local consensus, in the context of distributed systems and machine learning, focuses on achieving agreement among multiple agents or nodes, each possessing partial or noisy information. Current research emphasizes developing algorithms and model architectures that efficiently achieve consensus despite challenges like intermittent connectivity, data heterogeneity, and privacy constraints, often employing techniques like collaborative relaying, contrastive learning, and weighted averaging to improve robustness and convergence. This work is significant for advancing distributed computation and machine learning, enabling more efficient and reliable training of models across networks of devices or agents, with applications ranging from federated learning to multi-view data analysis.