Federated learning (FL) has been widely adopted in various fields of study and business. Traditional centralized FL systems suffer from serious issues. To address these concerns, decentralized federated learning (DFL) systems have been introduced in recent years. With the help of blockchains, they attempt to achieve more integrity and efficiency. However, privacy preservation remains an uncovered aspect of these systems. To tackle this, as well as to scale the blockchain-based computations, we propose a zero-knowledge proof (ZKP)-based aggregator (zkDFL). This allows clients to share their large-scale model parameters with a trusted centralized server without revealing their individual data to other clients. We utilize blockchain technology to manage the aggregation algorithm via smart contracts. The server performs a ZKP algorithm to prove to the clients that the aggregation is done according to the accepted algorithm. Additionally, the server can prove that all inputs from clients have been used. We evaluate our approach using a public dataset related to the wearable Internet of Things. As demonstrated by numerical evaluations, zkDFL introduces verifiability of the correctness of the aggregation process and enhances the privacy protection and scalability of DFL systems, while the gas cost has significantly declined.

翻译：联邦学习已在众多学术与商业领域得到广泛应用。传统集中式联邦学习系统存在严重缺陷。为解决这些问题，近年来引入了去中心化联邦学习系统。借助区块链技术，此类系统致力于增强完整性与效率。然而，隐私保护仍是这些系统中尚未充分解决的关键问题。为此，并为了扩展基于区块链的计算能力，我们提出了一种基于零知识证明的聚合器（zkDFL）。该方案允许客户端将大规模模型参数共享给可信集中服务器，同时避免向其他客户端泄露个体数据。我们利用区块链技术通过智能合约管理聚合算法，服务器执行零知识证明算法向客户端证明聚合过程严格遵循既定算法，同时可证明所有客户端的输入均被使用。基于可穿戴物联网公共数据集的评估表明，zkDFL在显著降低Gas成本的同时，实现了聚合过程正确性的可验证性，并增强了去中心化联邦学习系统的隐私保护能力与可扩展性。