Blockchain promises to enhance distributed machine learning (ML) approaches such as federated learning (FL) by providing further decentralization, security, immutability, and trust, which are key properties for enabling collaborative intelligence in next-generation applications. Nonetheless, the intrinsic decentralized operation of peer-to-peer (P2P) blockchain nodes leads to an uncharted setting for FL, whereby the concepts of FL round and global model become meaningless, as devices' synchronization is lost without the figure of a central orchestrating server. In this paper, we study the practical implications of outsourcing the orchestration of FL to a democratic network such as in a blockchain. In particular, we focus on the effects that model staleness and inconsistencies, endorsed by blockchains' modus operandi, have on the training procedure held by FL devices asynchronously. Using simulation, we evaluate the blockchained FL operation on the well-known CIFAR-10 dataset and focus on the accuracy and timeliness of the solutions. Our results show the high impact of model inconsistencies on the accuracy of the models (up to a ~35% decrease in prediction accuracy), which underscores the importance of properly designing blockchain systems based on the characteristics of the underlying FL application.

翻译：区块链通过在分布式机器学习（如联邦学习）中实现进一步去中心化、安全性、不可篡改性和可信度，有望增强这类方法，这些特性是实现下一代应用中协作智能的关键属性。然而，点对点区块链节点的内在去中心化操作给联邦学习带来了一个未探索的场景——在没有中心协调服务器的情况下，设备同步性丧失，导致联邦学习轮次和全局模型等概念失去意义。本文研究了将联邦学习协调外包给区块链这类民主化网络的实践影响。具体而言，我们关注区块链运作模式所导致的模型陈旧性与不一致性对非同步联邦学习设备训练过程的影响。通过仿真实验，我们在经典CIFAR-10数据集上评估了区块链联邦学习的运行效果，重点分析了解决方案的准确性和时效性。结果表明，模型不一致性对模型准确率具有显著影响（预测准确率下降高达约35%），这凸显了根据底层联邦学习应用特性合理设计区块链系统的重要性。