Gossip Algorithm

Gossip algorithms are decentralized communication protocols enabling collaborative computation across networks of agents without a central server, primarily aiming to achieve consensus or distributed optimization. Current research focuses on improving the robustness and efficiency of these algorithms, particularly in asynchronous and unreliable network environments, employing techniques like random walks, and addressing challenges such as Byzantine failures and communication compression. This work has significant implications for various fields, including federated learning, distributed machine learning, and sensor networks, by offering privacy-preserving and scalable solutions for large-scale data processing and model training.