부산대학교 도서관

We discuss future directions of Blockchain as a collaborative value co-creation platform, in which network participants can gain extra insights that cannot be accessed when disconnected from the others. As such, we propose a decentralized machine learning framework that is carefully designed to respect the values of democracy, diversity, and privacy. Specifically, we propose a federated multi-task learning framework that integrates a privacy-preserving dynamic consensus algorithm. We show that a specific network topology called the expander graph dramatically improves the scalability of global consensus building. We conclude the paper by making some remarks on open problems.
Comment: Tsuyoshi Id\'e and Rudy Raymond, "Decentralized Collaborative Learning with Probabilistic Data Protection," In Proceedings of the 2021 IEEE International Conference on Smart Data Services (SMDS 21, September 5-10, 2021, virtual), pp.234-243