Federated Learning (FL) has gained widespread popularity in recent years due to the fast booming of advanced machine learning and artificial intelligence along with emerging security and privacy threats. FL enables efficient model generation from local data storage of the edge devices without revealing the sensitive data to any entities. While this paradigm partly mitigates the privacy issues of users' sensitive data, the performance of the FL process can be threatened and reached a bottleneck due to the growing cyber threats and privacy violation techniques. To expedite the proliferation of FL process, the integration of blockchain for FL environments has drawn prolific attention from the people of academia and industry. Blockchain has the potential to prevent security and privacy threats with its decentralization, immutability, consensus, and transparency characteristic. However, if the blockchain mechanism requires costly computational resources, then the resource-constrained FL clients cannot be involved in the training. Considering that, this survey focuses on reviewing the challenges, solutions, and future directions for the successful deployment of blockchain in resource-constrained FL environments. We comprehensively review variant blockchain mechanisms that are suitable for FL process and discuss their trade-offs for a limited resource budget. Further, we extensively analyze the cyber threats that could be observed in a resource-constrained FL environment, and how blockchain can play a key role to block those cyber attacks. To this end, we highlight some potential solutions towards the coupling of blockchain and federated learning that can offer high levels of reliability, data privacy, and distributed computing performance.

翻译：联邦学习（FL）近年来因先进机器学习与人工智能的快速发展，以及新兴安全与隐私威胁的涌现而广受欢迎。FL能够利用边缘设备的本地数据存储高效生成模型，同时无需向任何实体泄露敏感数据。尽管该范式部分缓解了用户敏感数据的隐私问题，但FL过程的性能可能因日益增长的网络安全威胁与隐私侵犯技术而受到威胁并达到瓶颈。为促进FL过程的普及，将区块链集成至FL环境已引起学术界与工业界的广泛关注。区块链凭借其去中心化、不可篡改、共识机制与透明性特征，具有防范安全与隐私威胁的潜力。然而，若区块链机制需要高昂计算资源，则资源受限的FL客户端将无法参与训练。基于此，本综述聚焦于区块链在资源受限FL环境中成功部署所面临的挑战、解决方案及未来方向。我们全面梳理了适用于FL过程的多种区块链机制，并探讨其在有限资源预算下的权衡。进一步，我们深入分析了资源受限FL环境中可能出现的网络威胁，以及区块链如何关键性地阻断这些网络攻击。最后，我们提出了若干潜在解决方案，以实现区块链与联邦学习的深度耦合，从而提供高可靠性、数据隐私保护与分布式计算性能。