arXiv paper proposes privacy-preserving gossip learning with sequential updates

A new arXiv preprint describes a decentralized learning setup where each agent keeps one private data sample alongside a shared model, and samples are processed one after another. Each update is designed to keep the model's predictions at already-learned samples unchanged, which the authors present as a way to limit forgetting while protecting privacy. The work sits at the intersection of gossip-style distributed training and privacy-preserving machine learning.