A Privacy-Preserving Federated Learning Framework for Blockchain Networks

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Abstract In this paper we introduce a scalable, privacy-preserving, federated learningframework, coined FLoBC, based on the concept of distributed ledgers underlyingblockchains. This is motivated by the rapid growth of data worldwide, especiallydecentralized data which calls for scalable, decenteralized machine learning mod-els which is capable of preserving the privacy of the data of the participatingusers. Towards this objective, we first motivate and define the problem scope. Wethen introduce the proposed FLoBC system architecture hinging on a number ofkey pillars, namely parallelism, decentralization and node update synchroniza-tion. In particular, we examine a number of known node update synchronizationpolicies and examine their performance merits and design trade-offs. Finally, wecompare the proposed federated learning system to a centralized learning systembaseline to demonstrate its performance merits. Our main finding in this paperis that our proposed decentralized learning framework was able to achieve com-parable performance to a classic centralized learning system, while distributingthe model training process across multiple nodes without sharing their actualdata. This provides a scalable, privacy-preserving solution for training a varietyof large machine learning models.

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