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We discuss future directions of Blockchain as a collaborative valueco-creation platform, in which network participants can gain extra insightsthat cannot be accessed when disconnected from the others. As such, we proposea decentralized machine learning framework that is carefully designed torespect the values of democracy, diversity, and privacy. Specifically, wepropose a federated multi-task learning framework that integrates aprivacy-preserving dynamic consensus algorithm. We show that a specific networktopology called the expander graph dramatically improves the scalability ofglobal consensus building. We conclude the paper by making some remarks on openproblems.

Decentralized Collaborative Learning with Probabilistic Data Protection