mlsanityData to 7 Oct 2026

On the Decentralization of Blockchain-enabled Asynchronous Federated Learning

arXiv 2205.10201 ↗First posted 20 May 2022cs.CR cs.AI cs.DCcited by 3

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Abstract (arXiv)

Federated learning (FL), thanks in part to the emergence of the edge computing paradigm, is expected to enable true real-time applications in production environments. However, its original dependence on a central server for orchestration raises several concerns in terms of security, privacy, and scalability. To solve some of these worries, blockchain technology is expected to bring decentralization, robustness, and enhanced trust to FL. The empowerment of FL through blockchain (also referred to as FLchain), however, has some implications in terms of ledger inconsistencies and age of information (AoI), which are naturally inherited from the blockchain's fully decentralized operation. Such issues stem from the fact that, given the temporary ledger versions in the blockchain, FL devices may use different models for training, and that, given the asynchronicity of the FL operation, stale local updates (computed using outdated models) may be generated. In this paper, we shed light on the implications of the FLchain setting and study the effect that both the AoI and ledger inconsistencies have on the FL performance. To that end, we provide a faithful simulation tool that allows capturing the decentralized and asynchronous nature of the FLchain operation.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 13 of its 35 references that are in the map. First posted on arXiv 20 May 2022.

Builds on

13 of 35 references.

References · 13

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  5. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  6. 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 225
  7. 2019BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learningcited by 115
  8. 2018Blockchained On-Device Federated Learningcited by 105
  9. 2021Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challengescited by 75
  10. 2020Age of Information: An Introduction and Surveycited by 64
  11. 2019Decentralized Federated Learning: A Segmented Gossip Approachcited by 63
  12. 2020Ownership preserving AI Market Places using Blockchaincited by 5
1 more
  1. 2022End-to-End Latency Analysis and Optimal Block Size of Proof-of-Work Blockchain Applicationscited by 3

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Cited by year

Citing papers per year: 2022: 1; 2023: 2.

3 papers.

  1. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
  2. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  3. 2023The Implications of Decentralization in Blockchained Federated Learning: Evaluating the Impact of Model Staleness and Inconsistenciescited by 0