mlsanityData to 14 Oct 2026

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning

arXiv 2508.10042 ↗First posted 11 Aug 2025cs.CR cs.AIcited by 0

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

Abstract (arXiv)

Federated learning enhances traditional deep learning by enabling the joint training of a model with the use of IoT device's private data. It ensures privacy for clients, but is susceptible to data poisoning attacks during training that degrade model performance and integrity. Current poisoning detection methods in federated learning lack a standardized detection method or take significant liberties with trust. In this paper, we present Sys, a novel blockchain-enabled poison detection framework in federated learning. The framework decentralizes the role of the global server across participating clients. We introduce a judge model used to detect data poisoning in model updates. The judge model is produced by each client and verified to reach consensus on a single judge model. We implement our solution to show Sys is robust against data poisoning attacks and the creation of our judge model is scalable.

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 11 of its 21 references that are in the map. First posted on arXiv 11 Aug 2025.

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11 of 21 references.

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Same direction, newest first

  1. 2023Recursive Euclidean Distance Based Robust Aggregation Technique For Federated Learning20 Mar

Other references · 10

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Robust Aggregation for Federated Learningcited by 317
  3. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 275
  4. 2023Revisiting Weighted Aggregation in Federated Learning with Neural Networkscited by 58
  5. 2021Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocationcited by 56
  6. 2020BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecturecited by 20
  7. 2022Trustworthy Federated Learning via Blockchaincited by 15
  8. 2021Untargeted Poisoning Attack Detection in Federated Learning via Behavior Attestationcited by 13
  9. 2022HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detectioncited by 5
  10. 2022Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applicationscited by 4

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  1. 2025SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning
  2. 2024FedBlock: A Blockchain Approach to Federated Learning against Backdoor Attacks
  3. 2022FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information
  4. 2022Federated Learning based on Defending Against Data Poisoning Attacks in IoT
  5. 2024Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning
  6. 2022FLock: Defending Malicious Behaviors in Federated Learning with Blockchain
  7. 2023FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning
  8. 2025Krum Federated Chain (KFC): Using blockchain to defend against adversarial attacks in Federated Learning