mlsanityData to 14 Oct 2026

Enhancing Split Learning with Sharded and Blockchain-Enabled SplitFed Approaches

arXiv 2509.25555 ↗First posted 29 Sep 2025cs.DC cs.LGcited by 0

On the map: LLM safety and security → Federated unlearning and poisoning

Abstract (arXiv)

Collaborative and distributed learning techniques, such as Federated Learning (FL) and Split Learning (SL), hold significant promise for leveraging sensitive data in privacy-critical domains. However, FL and SL suffer from key limitations -- FL imposes substantial computational demands on clients, while SL leads to prolonged training times. To overcome these challenges, SplitFed Learning (SFL) was introduced as a hybrid approach that combines the strengths of FL and SL. Despite its advantages, SFL inherits scalability, performance, and security issues from SL. In this paper, we propose two novel frameworks: Sharded SplitFed Learning (SSFL) and Blockchain-enabled SplitFed Learning (BSFL). SSFL addresses the scalability and performance constraints of SFL by distributing the workload and communication overhead of the SL server across multiple parallel shards. Building upon SSFL, BSFL replaces the centralized server with a blockchain-based architecture that employs a committee-driven consensus mechanism to enhance fairness and security. BSFL incorporates an evaluation mechanism to exclude poisoned or tampered model updates, thereby mitigating data poisoning and model integrity attacks. Experimental evaluations against baseline SL and SFL approaches show that SSFL improves performance and scalability by 31.2% and 85.2%, respectively. Furthermore, BSFL increases resilience to data poisoning attacks by 62.7% while maintaining superior performance under normal operating conditions. To the best of our knowledge, BSFL is the first blockchain-enabled framework to implement an end-to-end decentralized SplitFed Learning system.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Federated unlearning and poisoning, one of 12 directions in LLM safety and security. Builds on 18 of its 31 references that are in the map. First posted on arXiv 29 Sep 2025.

Builds on

18 of 31 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  5. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 395
  6. 2020SplitFed: When Federated Learning Meets Split Learningcited by 310
  7. 2018Distributed learning of deep neural network over multiple agentscited by 279
  8. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 210
  9. 2020Threats to Federated Learning: A Surveycited by 178
  10. 2018Blockchained On-Device Federated Learningcited by 106
  11. 2020Unleashing the Tiger: Inference Attacks on Split Learningcited by 105
  12. 2020A Blockchain-based Decentralized Federated Learning Framework with Committee Consensuscited by 84
6 more
  1. 2020End-to-End Evaluation of Federated Learning and Split Learning for Internet of Thingscited by 64
  2. 2020SplitNN-driven Vertical Partitioningcited by 42
  3. 2019Proof of Federated Learning: A Novel Energy-recycling Consensus Algorithmcited by 26
  4. 2021Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learningcited by 23
  5. 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
  6. 2024Proof-of-Collaborative-Learning: A Multi-winner Federated Learning Consensus Algorithmcited by 2

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  3. 2025FuSeFL: Fully Secure and Scalable Federated Learning
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  5. 2024BlockFUL: Enabling Unlearning in Blockchained Federated Learning
  6. 2022BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning
  7. 2025Krum Federated Chain (KFC): Using blockchain to defend against adversarial attacks in Federated Learning
  8. 2022Scalable Collaborative Learning via Representation Sharing