mlsanityData to 14 Oct 2026

Blockchain-based Secure Client Selection in Federated Learning

arXiv 2205.05611 ↗First posted 11 May 2022cs.CR cs.LGcited by 5

Not on the map. Why?

Abstract (arXiv)

Despite the great potential of Federated Learning (FL) in large-scale distributed learning, the current system is still subject to several privacy issues due to the fact that local models trained by clients are exposed to the central server. Consequently, secure aggregation protocols for FL have been developed to conceal the local models from the server. However, we show that, by manipulating the client selection process, the server can circumvent the secure aggregation to learn the local models of a victim client, indicating that secure aggregation alone is inadequate for privacy protection. To tackle this issue, we leverage blockchain technology to propose a verifiable client selection protocol. Owing to the immutability and transparency of blockchain, our proposed protocol enforces a random selection of clients, making the server unable to control the selection process at its discretion. We present security proofs showing that our protocol is secure against this attack. Additionally, we conduct several experiments on an Ethereum-like blockchain to demonstrate the feasibility and practicality of our solution.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 12 of its 31 references that are in the map. First posted on arXiv 11 May 2022.

Builds on

12 of 31 references.

References · 12

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016Deep Learning with Differential Privacycited by 2,728
  3. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  4. 2020The Future of Digital Health with Federated Learningcited by 633
  5. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  6. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  7. 2018ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Modelscited by 498
  8. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 204
  9. 2018Blockchained On-Device Federated Learningcited by 106
  10. 2019Reliable Federated Learning for Mobile Networkscited by 92
  11. 2020When Federated Learning Meets Blockchain: A New Distributed Learning Paradigmcited by 31
  12. 2021A Dispersed Federated Learning Framework for 6G-Enabled Autonomous Driving Carscited by 6

Built on by

Cited by year

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

5 papers.

  1. 2023Active Membership Inference Attack under Local Differential Privacy in Federated Learningcited by 17
  2. 2024Analysis of Privacy Leakage in Federated Large Language Modelscited by 9
  3. 2025AdRo-FL: Informed and Secure Client Selection for Federated Learning in the Presence of Adversarial Aggregatorcited by 1
  4. 2023OASIS: Offsetting Active Reconstruction Attacks in Federated Learningcited by 0
  5. 2025Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Modelscited by 0