mlsanityData to 14 Oct 2026

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

arXiv 2507.07637 ↗First posted 10 Jul 2025cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 40 references that are in the map. First posted on arXiv 10 Jul 2025.

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18 of 40 references.

References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2019Advances and Open Problems in Federated Learningcited by 3,046
  4. 2019Deep Leakage from Gradientscited by 1,021
  5. 2020SplitFed: When Federated Learning Meets Split Learningcited by 310
  6. 2018Distributed learning of deep neural network over multiple agentscited by 279
  7. 2019Split Learning for collaborative deep learning in healthcarecited by 73
  8. 2021Eluding Secure Aggregation in Federated Learning via Model Inconsistencycited by 67
  9. 2020Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?cited by 50
  10. 2018Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchainscited by 49
  11. 2019BAFFLE : Blockchain Based Aggregator Free Federated Learningcited by 38
  12. 2023Privacy and Efficiency of Communications in Federated Split Learningcited by 21
6 more
  1. 2017BLOCKBENCH: A Framework for Analyzing Private Blockchainscited by 5
  2. 2023Tackling the Non-IID Issue in Heterogeneous Federated Learning by Gradient Harmonizationcited by 4
  3. 2023Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Thingscited by 3
  4. 2020A Transactional Perspective on Execute-order-validate Blockchainscited by 1
  5. 2023Split Federated Learning: Speed up Model Training in Resource-Limited Wireless Networkscited by 1
  6. 2024Wait or Not to Wait: Evaluating Trade-Offs between Speed and Precision in Blockchain-based Federated Aggregationcited by 1

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