论文标题
通过概率数据保护分散的协作学习
Decentralized Collaborative Learning with Probabilistic Data Protection
论文作者
论文摘要
我们讨论了区块链作为协作价值共同创建平台的未来方向，在该平台中，网络参与者可以获得与其他人断开连接时无法访问的额外见解。因此，我们提出了一个分散的机器学习框架，该框架经过精心设计，以尊重民主，多样性和隐私的价值。具体而言，我们提出了一个联合的多任务学习框架，该框架集成了隐私保护动态共识算法。我们表明，一个称为expander图的特定网络拓扑显着提高了全球共识构建的可扩展性。我们通过对开放问题作了一些评论来结束论文。
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.
