Decentralized Collaborative Learning with Probabilistic Data Protection

• URL: http://arxiv.org/abs/2208.10674v2
• Date: Wed, 24 Aug 2022 01:21:01 GMT
• Title: Decentralized Collaborative Learning with Probabilistic Data Protection
• Abstract summary: 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.
• Score: 4.7362989868031855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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