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.

翻译：我们讨论 " 屏障链 " 的未来方向,将其作为一个合作价值共创平台,网络参与者可以在其中获得更多的洞察力,而这种洞察力在与其他人脱节时是无法获取的。因此,我们提议一个分散的机器学习框架,仔细设计,以尊重民主、多样性和隐私的价值观。具体地说,我们提议一个结合一个隐私保护动态共识算法的联结多任务学习框架。我们表明,一个称为 " 扩张式图案 " 的具体网络布局极大地改善了全球建立共识的可扩展性。我们通过就开放问题发表一些意见来结束论文。