mlsanityData to 7 Oct 2026

FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks

arXiv 2310.02113 ↗First posted 3 Oct 2023cs.CR cs.DC cs.LGcited by 4

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Abstract (arXiv)

Federated learning (FL) is a distributed learning process that uses a trusted aggregation server to allow multiple parties (or clients) to collaboratively train a machine learning model without having them share their private data. Recent research, however, has demonstrated the effectiveness of inference and poisoning attacks on FL. Mitigating both attacks simultaneously is very challenging. State-of-the-art solutions have proposed the use of poisoning defenses with Secure Multi-Party Computation (SMPC) and/or Differential Privacy (DP). However, these techniques are not efficient and fail to address the malicious intent behind the attacks, i.e., adversaries (curious servers and/or compromised clients) seek to exploit a system for monetization purposes. To overcome these limitations, we present a ledger-based FL framework known as FLEDGE that allows making parties accountable for their behavior and achieve reasonable efficiency for mitigating inference and poisoning attacks. Our solution leverages crypto-currency to increase party accountability by penalizing malicious behavior and rewarding benign conduct. We conduct an extensive evaluation on four public datasets: Reddit, MNIST, Fashion-MNIST, and CIFAR-10. Our experimental results demonstrate that (1) FLEDGE provides strong privacy guarantees for model updates without sacrificing model utility; (2) FLEDGE can successfully mitigate different poisoning attacks without degrading the performance of the global model; and (3) FLEDGE offers unique reward mechanisms to promote benign behavior during model training and/or model aggregation.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 39 of its 72 references that are in the map. First posted on arXiv 3 Oct 2023.

Builds on

39 of 72 references.

References · 39

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27 more
  1. 2021PPFL: Privacy-preserving Federated Learning with Trusted Execution Environmentscited by 93
  2. 2021CRFL: Certifiably Robust Federated Learning against Backdoor Attackscited by 91
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  4. 2020A Blockchain-based Decentralized Federated Learning Framework with Committee Consensuscited by 83
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  18. 2021DeSMP: Differential Privacy-exploited Stealthy Model Poisoning Attacks in Federated Learningcited by 11
  19. 2022RFLBAT: A Robust Federated Learning Algorithm against Backdoor Attackcited by 9
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Built on by

Cited by year

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

4 papers.

  1. 2023FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learningcited by 25
  2. 2025SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)cited by 7
  3. 2024Adversarially Guided Stateful Defense Against Backdoor Attacks in Federated Deep Learningcited by 5
  4. 2025FL-CLEANER: byzantine and backdoor defense by CLustering Errors of Activation maps in Non-iid fedErated leaRningcited by 0