【Author】
Li, Yuzheng; Chen, Chuan; Liu, Nan; Huang, Huawei; Zheng, Zibin; Yan, Qiang
【Abstract】Federated learning has been widely studied and applied to various scenarios, such as financial credit, medical identification, and so on. Under these settings, federated learning protects users from exposing their private data, while cooperatively training a shared machine learning algorithm model (i.e., the global model) for a variety of realworld applications. The only data exchanged is the gradient of the model or the updated model (i.e., the local model update). However, the security of federated learning is increasingly being questioned, due to the malicious clients or central servers constant attack on the global model or user privacy data. To address these security issues, we propose a decentralized federated learning framework based on blockchain, that is, a Block-chain-based Federated Learning framework with Committee consensus (BFLC). Without a centralized server, the framework uses blockchain for the global model storage and the local model update exchange. To enable the proposed BFLC, we also devise an innovative committee consensus mechanism, which can effectively reduce the amount of consensus computing and reduce malicious attacks. We then discuss the scalability of BFLC, including theoretical security, storage optimization, and incentives. Finally, based on a FISCO blockchain system, we perform experiments using an AlexNet model on several frameworks with a real-world dataset FEMNIST. The experimental results demonstrate the effectiveness and security of the BFLC framework.
【Keywords】Servers; Blockchain; Training; Data models; Security; Collaborative work; Task analysis
【标题】具有委员会共识的基于区块链的去中心化联邦学习框架
【摘要】联邦学习已被广泛研究并应用于金融信用、医疗识别等各种场景。在这些设置下，联邦学习保护用户不暴露他们的私人数据，同时为各种现实世界的应用程序合作训练一个共享的机器学习算法模型（即全局模型）。唯一交换的数据是模型的梯度或更新的模型（即本地模型更新）。然而，由于恶意客户端或中央服务器不断攻击全局模型或用户隐私数据，联邦学习的安全性越来越受到质疑。为了解决这些安全问题，我们提出了一种基于区块链的去中心化联邦学习框架，即基于区块链的具有委员会共识的联邦学习框架（BFLC）。在没有集中式服务器的情况下，该框架使用区块链进行全局模型存储和本地模型更新交换。为了使提议的 BFLC 成为可能，我们还设计了一种创新的委员会共识机制，可以有效减少共识计算量，减少恶意攻击。然后我们讨论 BFLC 的可扩展性，包括理论安全性、存储优化和激励。最后，基于 FISCO 区块链系统，我们使用 AlexNet 模型在具有真实数据集 FEMNIST 的多个框架上进行实验。实验结果证明了 BFLC 框架的有效性和安全性。
【关键词】服务器；区块链；训练;数据模型；安全;协作工作；任务分析
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