ZIP-DL introduces a novel privacy-aware decentralized learning algorithm using correlated noise to enhance privacy without compromising model accuracy.
SHATTER, a novel decentralized learning approach, enhances privacy by having nodes operate multiple virtual nodes that communicate random model chunks through a dynamically changing communication topology, preventing attackers from collecting full models or identifying the original node behind a given model chunk.