Peer to Peer

Peer-to-peer (P2P) systems are decentralized networks enabling collaborative tasks without relying on a central server, addressing concerns about single points of failure and data privacy. Current research focuses on developing robust P2P algorithms for machine learning, including federated learning and graph neural networks, with a particular emphasis on addressing challenges like data heterogeneity, Byzantine failures, and maintaining privacy while ensuring efficient model training and convergence. These advancements have significant implications for various fields, from improving energy efficiency in smart grids and enabling collaborative robotics to enhancing the security and scalability of AI applications.