Communication Free

Communication-free approaches aim to solve computational problems where agents must achieve a common goal without exchanging information, relying instead on shared randomness or inherent system structure. Current research focuses on developing and analyzing algorithms for tasks like coupling probability distributions, achieving consensus in decentralized systems, and performing federated learning, often employing techniques such as weighted MinHash, Gumbel sampling, and topological reasoning to guide agent behavior. These methods are significant for improving efficiency and robustness in distributed systems, particularly in scenarios with communication constraints or privacy concerns, with applications ranging from large language model acceleration to multi-robot path planning.