【Author】
Li, Ziyuan; Liu, Jian; Hao, Jialu; Wang, Huimei; Xian, Ming
【Abstract】Over the years, the flourish of crowd computing has enabled enterprises to accomplish computing tasks through crowdsourcing in a large-scale and high-quality manner, and therefore how to efficiently and securely implement crowd computing becomes a hotspot. Some recent work innovatively adopted a P2P (peer-to-peer) network as the communication environment of crowdsourcing. Based on its decentralized control, issues like single-point-of-failure or DDoS attack can be overcome to some extent, but the huge computing capacity and storage costs required by this scheme is always unbearable. Federated learning is a distributed machine learning that supports local storage of data, and clients implement training through interactive gradient values. In our work, we combine blockchain with federated learning and propose a crowdsourcing framework named CrowdSFL, that users can implement crowdsourcing with less overhead and higher security. In addition, to protect the privacy of participants, we design a new re-encryption algorithm based on Elgamal to ensure that interactive values and other information will not be exposed to other participants outside the workflow. Finally, we have proved through experiments that our framework is superior to some similar work in accuracy, efficiency, and overhead.
【Keywords】crowd computing; blockchain; federated learning; re-encryption algorithm
【标题】CrowdSFL: 基于区块链和联邦学习的安全群组计算框架
【摘要】多年来，群组计算的蓬勃发展使得企业可以通过众包的方式大规模、高质量地完成计算任务，如何高效、安全地实现人群计算成为热点。最近的一些工作创新地采用了 P2P（点对点）网络作为众包的通信环境。基于它的去中心化控制，在一定程度上可以克服单点故障或DDoS攻击等问题，但这种方案所需的巨大计算能力和存储成本总是难以承受。联邦学习是一种分布式机器学习，支持本地存储数据，客户端通过交互式梯度值实现训练。在我们的工作中，我们将区块链与联邦学习相结合，提出了一个名为 CrowdSFL 的众包框架，用户可以以更少的开销和更高的安全性实现众包。此外，为了保护参与者的隐私，我们基于 Elgamal 设计了一种新的重加密算法，以确保交互值和其他信息不会暴露给工作流程之外的其他参与者。最后，我们通过实验证明，我们的框架在准确性、效率和开销方面优于一些类似的工作。
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