论文标题
ScaleSFL：用于基于区块链的联合学习的碎片解决方案
ScaleSFL: A Sharding Solution for Blockchain-Based Federated Learning
论文作者
论文摘要
在过去的几年中，基于区块链的联合学习在过去几年中引起了人们的重大兴趣，因为对数据隐私，机器学习的进步和区块链创新的关注不断增加。但是，安全性和可伸缩性的差距阻碍了现实应用程序的开发。在这项研究中，我们提出了ScaleSFL，这是一种可扩展的基于区块链的碎片解决方案，用于联合学习。 ScaleSFL通过将外联合学习组件分开以验证模型更新而不是控制整个联合学习流程来支持互操作性。我们使用HyperLeDger Fabric实施了ScaleSFL作为概念验证原型系统，以证明解决方案的可行性。我们提出了通过模型创建的HyperLeDger Caliper基准测试工具收集的结果的性能评估。我们的评估结果表明，碎片可以线性地提高验证性能，同时保持高效和安全。
Blockchain-based federated learning has gained significant interest over the last few years with the increasing concern for data privacy, advances in machine learning, and blockchain innovation. However, gaps in security and scalability hinder the development of real-world applications. In this study, we propose ScaleSFL, which is a scalable blockchain-based sharding solution for federated learning. ScaleSFL supports interoperability by separating the off-chain federated learning component in order to verify model updates instead of controlling the entire federated learning flow. We implemented ScaleSFL as a proof-of-concept prototype system using Hyperledger Fabric to demonstrate the feasibility of the solution. We present a performance evaluation of results collected through Hyperledger Caliper benchmarking tools conducted on model creation. Our evaluation results show that sharding can improve validation performance linearly while remaining efficient and secure.
