mlsanityData to 14 Oct 2026

FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks

arXiv 2308.05832 ↗First posted 10 Aug 2023cs.CR cs.LGcited by 15

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Abstract (arXiv)

Federated learning (FL) is revolutionizing how we learn from data. With its growing popularity, it is now being used in many safety-critical domains such as autonomous vehicles and healthcare. Since thousands of participants can contribute in this collaborative setting, it is, however, challenging to ensure security and reliability of such systems. This highlights the need to design FL systems that are secure and robust against malicious participants' actions while also ensuring high utility, privacy of local data, and efficiency. In this paper, we propose a novel FL framework dubbed as FLShield that utilizes benign data from FL participants to validate the local models before taking them into account for generating the global model. This is in stark contrast with existing defenses relying on server's access to clean datasets -- an assumption often impractical in real-life scenarios and conflicting with the fundamentals of FL. We conduct extensive experiments to evaluate our FLShield framework in different settings and demonstrate its effectiveness in thwarting various types of poisoning and backdoor attacks including a defense-aware one. FLShield also preserves privacy of local data against gradient inversion attacks.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 40 of its 78 references that are in the map. First posted on arXiv 10 Aug 2023.

Builds on

40 of 78 references.

References · 40

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  4. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  5. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  6. 2017Understanding Black-box Predictions via Influence Functionscited by 1,741
  7. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  8. 2017Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoningcited by 1,055
  9. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  10. 2019Towards Federated Learning at Scale: System Designcited by 832
  11. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  12. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
28 more
  1. 2020The Future of Digital Health with Federated Learningcited by 633
  2. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  3. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 493
  4. 2020iDLG: Improved Deep Leakage from Gradientscited by 382
  5. 2018Analyzing Federated Learning through an Adversarial Lenscited by 351
  6. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 346
  7. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 334
  8. 2021See through Gradients: Image Batch Recovery via GradInversioncited by 326
  9. 2019Robust Aggregation for Federated Learningcited by 317
  10. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 309
  11. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 275
  12. 2019Can You Really Backdoor Federated Learning?cited by 255
  13. 2018Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learningcited by 225
  14. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 210
  15. 2021Evaluating Gradient Inversion Attacks and Defenses in Federated Learningcited by 196
  16. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 176
  17. 2019Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulationcited by 141
  18. 2022FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clientscited by 125
  19. 2018Is feature selection secure against training data poisoning?cited by 93
  20. 2022DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspectioncited by 81
  21. 2019Byzantine-Robust Federated Machine Learning through Adaptive Model Averagingcited by 70
  22. 2020A Framework for Evaluating Gradient Leakage Attacks in Federated Learningcited by 64
  23. 2017Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descentcited by 59
  24. 2020Federated Learning Meets Multi-objective Optimizationcited by 53
  25. 2020BaFFLe: Backdoor detection via Feedback-based Federated Learningcited by 53
  26. 2022LoMar: A Local Defense Against Poisoning Attack on Federated Learningcited by 31
  27. 2019Shielding Collaborative Learning: Mitigating Poisoning Attacks through Client-Side Detectioncited by 26
  28. 2022FL-Defender: Combating Targeted Attacks in Federated Learningcited by 15

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 8; 2026: 4.

15 papers.

Sort:

  1. 2024Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learningcited by 2
  2. 2024Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoningcited by 1
  3. 2024VerifiableFL: Verifiable Claims for Federated Learning using Exclavescited by 1
  4. 2025Two Heads Are Better than One: Model-Weight and Latent-Space Analysis for Federated Learning on Non-iid Data against Poisoning Attackscited by 1
  5. 2026FedSurrogate: Backdoor Defense in Federated Learning via Layer Criticality and Surrogate Replacementcited by 1
  6. 2025Buffer is All You Need: Defending Federated Learning against Backdoor Attacks under Non-iids via Bufferingcited by 0
  7. 2025On the Inference (In-)Security of Vertical Federated Learning: Efficient Auditing against Inference Tampering Attackcited by 0
  8. 2025Self-Adaptive and Robust Federated Spectrum Sensing without Benign Majority for Cellular Networkscited by 0
  9. 2025FedBAP: Backdoor Defense via Benign Adversarial Perturbation in Federated Learningcited by 0
  10. 2025FLAT: Revealing Hidden Latent-Conditioned Backdoor Failures in Federated Learningcited by 0
  11. 2025Benchmarking Robust Aggregation in Decentralized Gradient Marketplacescited by 0
  12. 2025CoSIFL: Collaborative Secure and Incentivized Federated Learning with Differential Privacycited by 0
3 more
  1. 2026SecureAFL: Secure Asynchronous Federated Learningcited by 0
  2. 2026EnCAgg: Enhanced Clustering Aggregation for Robust Federated Learning against Dynamic Model Poisoningcited by 0
  3. 2026Your Privacy My Cloak: Backdoor Attacks on Differentially Private Federated Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025GShield: Mitigating Poisoning Attacks in Federated Learning
  2. 2025SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning
  3. 2024Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning
  4. 2025FL-CLEANER: byzantine and backdoor defense by CLustering Errors of Activation maps in Non-iid fedErated leaRning
  5. 2023FedDefender: Backdoor Attack Defense in Federated Learning
  6. 2025Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning
  7. 2023Mitigating Backdoors in Federated Learning with FLD
  8. 2023FedDefender: Client-Side Attack-Tolerant Federated Learning