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arXiv · 2507.07637

HLF-FSL. A Decentralized Federated Split Learning Solution for IoT on Hyperledger Fabric

Carlos Beis Penedo·Rebeca P. Díaz Redondo·Ana Fernández Vilas·Manuel Fernández Veiga·Francisco Troncoso Pastoriza
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Abstract

Collaborative machine learning in sensitive domains demands scalable, privacy preserving solutions for enterprise deployment. Conventional Federated Learning (FL) relies on a central server, introducing single points of failure and privacy risks, while Split Learning (SL) partitions models for privacy but scales poorly due to sequential training. We present a decentralized architecture that combines Federated Split Learning (FSL) with the permissioned blockchain Hyperledger Fabric (HLF). Our chaincode orchestrates FSL's split model execution and peer-to-peer aggregation without any central coordinator, leveraging HLF's transient fields and Private Data Collections (PDCs) to keep raw data and model activations private. On CIFAR-10 and MNIST benchmarks, HLF-FSL matches centralized FSL accuracy while reducing per epoch training time compared to Ethereum-based works. Performance and scalability tests show minimal blockchain overhead and preserved accuracy, demonstrating enterprise grade viability.

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Carlos Beis Penedo, Rebeca P. Díaz Redondo, Ana Fernández Vilas, Manuel Fernández Veiga, Francisco Troncoso Pastoriza. 2025-07-10. HLF-FSL. A Decentralized Federated Split Learning Solution for IoT on Hyperledger Fabric. https://arxiv.org/abs/2507.07637

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