Federated learning is a privacy-preserving machine learning technique that trains models across multiple devices holding local data samples without exchanging them. There are many challenging issues in federated learning, such as coordinating participants’ activities, arbitrating their benefits, and aggregating models. Most existing solutions employ a centralized approach, in which a trustworthy central authority is needed for coordination. Such an approach incurs many disadvantages, including vulnerability to attacks, lack of credibility, and difficulty in calculating rewards. Recently, blockchain was identified as a potential solution for addressing the abovementioned issues. Extensive research has been conducted, and many approaches, methods, and techniques have been proposed. There is a need for a systematic survey to examine how blockchain can empower federated learning. Although there are many surveys on federated learning, few of them cover blockchain as an enabling technology. This work comprehensively surveys challenges, solutions, and future directions for blockchain-empowered federated learning (BlockFed). First, we identify the critical issues in federated learning and explain why blockchain provides a potential approach to addressing these issues. Second, we categorize existing system models into three classes: decoupled, coupled, and overlapped, according to how the federated learning and blockchain functions are integrated. Then we compare the advantages and disadvantages of these three system models, regard the disadvantages as challenging issues in BlockFed, and investigate corresponding solutions. Finally, we identify and discuss the future directions, including open problems in BlockFed.

联邦学习是一种保护隐私的机器学习技术，它可以在不交换本地数据样本的情况下跨多个设备训练模型。联邦学习中存在许多具有挑战性的问题，例如协调参与者的活动、仲裁他们的利益以及聚合模型。大多数现有解决方案都采用集中式方法，其中需要一个值得信赖的中央机构进行协调。这种方法会带来许多缺点，包括易受攻击、缺乏可信度以及难以计算奖励。最近，区块链被确定为解决上述问题的潜在解决方案。已经进行了广泛的研究，并且已经提出了许多途径、方法和技术。需要进行系统的调查来检查区块链如何支持联邦学习。尽管有很多关于联邦学习的调查，但很少有人将区块链作为一种使能技术。这项工作全面调查了区块链授权的联邦学习 (BlockFed) 的挑战、解决方案和未来方向。首先，我们确定联邦学习中的关键问题，并解释为什么区块链提供了解决这些问题的潜在方法。其次，根据联邦学习和区块链功能的集成方式，我们将现有系统模型分为三类：解耦、耦合和重叠。然后我们比较了这三种系统模型的优缺点，将其缺点作为 BlockFed 中具有挑战性的问题，并研究了相应的解决方案。