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

Abstract

Collaborative machine learning in sensitive domains demands scalable, privacy-aware and access-controlled solutions for enterprise-grade 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 because of sequential training. We present HLF-FSL, a decentralized architecture that combines federated split learning (FSL) with the permissioned blockchain Hyperledger Fabric (HLF). Chaincode orchestrates split-model execution and peer-to-peer aggregation without a central coordinator, leveraging HLF’s transient fields and Private Data Collections (PDCs) to keep raw data and model activations off-chain and access-controlled. On CIFAR-10, MNIST and ImageNet-Mini, HLF-FSL matches the accuracy of a standard server-coordinated FSL baseline while reducing per-epoch training time versus Ethereum-based baselines. Performance and scalability tests quantify the Fabric coordination overhead via a component-level breakdown of SDK-facing latencies and communication volumes; empirically, this overhead increases wall-clock epoch time while preserving the same accuracy-vs-epoch behavior as a FedSplit Learning baseline.