Clinical trials and drug discovery would not be effective without the collaboration of institutions. Earlier, it has been at the cost of individual’s privacy. Several pacts and compliances have been enforced to avoid data breaches. The existing schemes collect the participant’s data to a central repository for learning predictions as the collaboration is indispensable for research advances. The current COVID pandemic has put a question mark on our existing setup where the existing data repository has proved to be obsolete. There is a need for contemporary data collection, processing, and learning. The smartphones and devices held by the last person of the society have also made them a potential contributor. It demands to design a distributed and decentralized Collaborative Learning system that would make the knowledge inference from every data point. Federated Learning [21], proposed by Google, brings the concept of in-place model training by keeping the data intact to the device. Though it is privacy-preserving in nature, however, it is susceptible to inference, poisoning, and Sybil attacks. Blockchain is a decentralized programming paradigm that provides a broader control of the system, making it attack resistant. It poses challenges of high computing power, storage, and latency. These emerging technologies can contribute to the desired learning system and motivate them to address their security and efficiency issues. This article systematizes the security issues in Federated Learning, its corresponding mitigation strategies, and Blockchain’s challenges. Further, a Blockchain-based Federated Learning architecture with two layers of participation is presented, which improves the global model accuracy and guarantees participant’s privacy. It leverages the channel mechanism of Blockchain for parallel model training and distribution. It facilitates establishing decentralized trust between the participants and the gateways using the Blockchain, which helps to have only honest participants.

中文翻译：

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用于医疗保健系统的隐私保护分散式学习框架

没有机构的合作，临床试验和药物发现不会有效。早些时候，这是以个人隐私为代价的。为了避免数据泄露，已经执行了几项协议和合规性。现有方案将参与者的数据收集到中央存储库以进行学习预测，因为协作对于研究进展是必不可少的。当前的 COVID 大流行给我们现有的设置打了一个问号，现有的数据存储库已被证明已经过时。需要当代数据收集、处理和学习。社会最后一人持有的智能手机和设备也使他们成为潜在的贡献者。它要求设计一个分布式和去中心化的协作学习系统，从每个数据点进行知识推理。谷歌提出的联邦学习[21]通过保持数据完整到设备，带来了就地模型训练的概念。尽管它本质上是保护隐私的，但是它很容易受到推理、中毒和 Sybil 攻击。区块链是一种去中心化编程范式，可提供对系统的更广泛控制，使其具有抗攻击性。它带来了高计算能力、存储和延迟的挑战。这些新兴技术可以为所需的学习系统做出贡献，并激励他们解决安全和效率问题。本文系统化了联邦学习中的安全问题及其相应的缓解策略，和区块链的挑战。此外，提出了一种基于区块链的具有两层参与的联邦学习架构，提高了全局模型的准确性并保证了参与者的隐私。它利用区块链的通道机制进行并行模型训练和分发。它有助于在参与者和使用区块链的网关之间建立分散的信任，这有助于只有诚实的参与者。