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 setting 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 by applying two different ML models (ranging from low to high complexity) on the well-known MNIST and CIFAR-10 datasets, respectively, 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：区块链通过提供进一步的去中心化、安全性、不可篡改性和信任性，有望增强联邦学习等分布式机器学习方法，这些特性是实现下一代应用协作智能的关键属性。然而，点对点区块链节点的固有去中心化操作为联邦学习带来了一个未知场景，在此场景中，联邦学习轮次和全局模型的概念变得毫无意义，因为设备在缺乏中央协调服务器的情况下失去了同步性。本文研究了将联邦学习协调外包给区块链等民主化环境时的实际影响。具体而言，我们重点分析了区块链运行模式所导致的模型陈旧性与不一致性对异步联邦学习设备训练过程的影响。通过仿真，我们分别对著名的MNIST和CIFAR-10数据集应用两种不同复杂度的机器学习模型，评估了区块链联邦学习的运行效果，并重点关注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%），这凸显了基于底层联邦学习应用特性合理设计区块链系统的重要性。