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

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) that we have coined as HLF-FSL. 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.