Blockchain has become a popular decentralized paradigm for various applications in the zero-trust environment. The core of the blockchain is the consensus protocol, which establishes consensus among all the participants. PoW (Proof-of-Work) is one of the most popular consensus protocols. However, the PoW consensus protocol which incentives the participants to use their computing power to solve a meaningless hash puzzle is continuously questioned as energy-wasting. To address these issues, we propose an efficient and secure consensus protocol based on proof of useful federated learning for blockchain (called FedChain). We first propose a secure and robust blockchain architecture that takes federated learning tasks as proof of work. Then a pool aggregation mechanism is integrated to improve the efficiency of the FedChain architecture. To protect model parameter privacy for each participant within a mining pool, a secret sharing-based ring-all reduce architecture is designed. We also introduce a data distribution-based federated learning model optimization algorithm to improve the model performance of FedChain. At last, a zero-knowledge proof-based federated learning model verification is introduced to preserve the privacy of federated learning participants while proving the model performance of federated learning participants. Our approach has been tested and validated through extensive experiments, demonstrating its performance.

翻译：区块链已成为零信任环境下各类应用场景中广受欢迎的去中心化范式。其核心在于共识协议——该协议在全体参与者之间建立共识。工作量证明（PoW）是最常用的共识协议之一，但因其激励参与者用算力解决无意义的哈希难题而被持续质疑为能源浪费。为解决这些问题，我们提出一种基于区块链实用联邦学习证明的高效安全共识协议（称为FedChain）。首先，我们创新性地提出一种以联邦学习任务作为工作量证明的安全稳健区块链架构；其次，整合池聚合机制以提升FedChain架构效率；为保护矿池内参与者的模型参数隐私，设计基于秘密共享的环全约简架构；同时引入基于数据分布的联邦学习模型优化算法改善FedChain模型性能；最后，提出基于零知识证明的联邦学习模型验证方法，在证明联邦学习参与者模型性能的同时保障其隐私。通过大量实验测试与验证，证明了我们方法的有效性。