The Implications of Decentralization in Blockchained Federated Learning: Evaluating the Impact of Model Staleness and Inconsistencies

• URL: http://arxiv.org/abs/2310.07471v2
• Date: Mon, 25 Mar 2024 11:07:13 GMT
• Title: The Implications of Decentralization in Blockchained Federated Learning: Evaluating the Impact of Model Staleness and Inconsistencies
• Authors: Francesc Wilhelmi, Nima Afraz, Elia Guerra, Paolo Dini,
• Abstract summary: We study the practical implications of outsourcing the orchestration of federated learning to a democratic setting such as in a blockchain. Using simulation, we evaluate the blockchained FL operation by applying two different ML models on the well-known MNIST and CIFAR-10 datasets. Our results show the high impact of model inconsistencies on the accuracy of the models (up to a 35% decrease in prediction accuracy)
• Score: 2.6391879803618115
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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