mlsanityData to 7 Oct 2026

Secure and Efficient Federated Learning Through Layering and Sharding Blockchain

arXiv 2104.13130 ↗First posted 27 Apr 2021cs.CR cs.AI cs.ITcited by 10

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Abstract (arXiv)

Introducing blockchain into Federated Learning (FL) to build a trusted edge computing environment for transmission and learning has attracted widespread attention as a new decentralized learning pattern. However, traditional consensus mechanisms and architectures of blockchain systems face significant challenges in handling large-scale FL tasks, especially on Internet of Things (IoT) devices, due to their substantial resource consumption, limited transaction throughput, and complex communication requirements. To address these challenges, this paper proposes ChainFL, a novel two-layer blockchain-driven FL system. It splits the IoT network into multiple shards within the subchain layer, effectively reducing the scale of information exchange, and employs a Direct Acyclic Graph (DAG)-based mainchain as the mainchain layer, enabling parallel and asynchronous cross-shard validation. Furthermore, the FL procedure is customized to integrate deeply with blockchain technology, and a modified DAG consensus mechanism is designed to mitigate distortion caused by abnormal models. To provide a proof-of-concept implementation and evaluation, multiple subchains based on Hyperledger Fabric and a self-developed DAG-based mainchain are deployed. Extensive experiments demonstrate that ChainFL significantly surpasses conventional FL systems, showing up to a 14% improvement in training efficiency and a threefold increase in robustness.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 21 of its 73 references that are in the map. First posted on arXiv 27 Apr 2021.

Builds on

21 of 73 references.

References · 21

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  4. 2019Towards Federated Learning at Scale: System Designcited by 826
  5. 2018How To Backdoor Federated Learningcited by 745
  6. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  7. 2019Asynchronous Federated Optimizationcited by 264
  8. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 138
  9. 2018Blockchained On-Device Federated Learningcited by 105
  10. 2019Reliable Federated Learning for Mobile Networkscited by 92
  11. 2019SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overheadcited by 82
  12. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
9 more
  1. 2018Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGDcited by 68
  2. 2014IPFS - Content Addressed, Versioned, P2P File Systemcited by 67
  3. 2018Learning Private Neural Language Modeling with Attentive Aggregationcited by 49
  4. 2020Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networkscited by 30
  5. 2020Adaptive Federated Learning and Digital Twin for Industrial Internet of Thingscited by 27
  6. 2019Proof of Federated Learning: A Novel Energy-recycling Consensus Algorithmcited by 26
  7. 2019PIRATE: A Blockchain-based Secure Framework of Distributed Machine Learning in 5G Networkscited by 18
  8. 2018Performance Analysis of the Raft Consensus Algorithm for Private Blockchainscited by 5
  9. 2019When Internet of Things Meets Blockchain: Challenges in Distributed Consensuscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 4; 2026: 2.

10 papers.

  1. 2024Trustworthy Federated Learning: Privacy, Security, and Beyondcited by 11
  2. 2023Blockchained Federated Learning for Internet of Things: A Comprehensive Surveycited by 5
  3. 2024Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutionscited by 5
  4. 2024Dataset Distillation-based Hybrid Federated Learning on Non-IID Datacited by 2
  5. 2025UnifyFL: Enabling Decentralized Cross-Silo Federated Learningcited by 1
  6. 2025RepuNet: A Reputation System for Mitigating Malicious Clients in DFLcited by 1
  7. 2026ZK-HybridFL: Zero-Knowledge Proof-Enhanced Hybrid Ledger for Federated Learningcited by 1
  8. 2025Combining Self-attention and Dilation Convolutional for Semantic Segmentation of Coal Maceral Groupscited by 0
  9. 2025SEDULity: A Proof-of-Learning Framework for Distributed and Secure Blockchains with Efficient Useful Workcited by 0
  10. 2026Secure Decentralized Federated Learning via Gossip and Virtual Votingcited by 0