mlsanityData to 14 Oct 2026

Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective

arXiv 2309.16456 ↗First posted 28 Sep 2023cs.LGcited by 6

Not on the map. Why?

Abstract (arXiv)

Existing approaches defend against backdoor attacks in federated learning (FL) mainly through a) mitigating the impact of infected models, or b) excluding infected models. The former negatively impacts model accuracy, while the latter usually relies on globally clear boundaries between benign and infected model updates. However, model updates are easy to be mixed and scattered throughout in reality due to the diverse distributions of local data. This work focuses on excluding infected models in FL. Unlike previous perspectives from a global view, we propose Snowball, a novel anti-backdoor FL framework through bidirectional elections from an individual perspective inspired by one principle deduced by us and two principles in FL and deep learning. It is characterized by a) bottom-up election, where each candidate model update votes to several peer ones such that a few model updates are elected as selectees for aggregation; and b) top-down election, where selectees progressively enlarge themselves through picking up from the candidates. We compare Snowball with state-of-the-art defenses to backdoor attacks in FL on five real-world datasets, demonstrating its superior resistance to backdoor attacks and slight impact on the accuracy of the global model.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 30 of its 50 references that are in the map. First posted on arXiv 28 Sep 2023.

Builds on

30 of 50 references.

References · 30

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  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. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
  6. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
  7. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  8. 2018Federated Learning with Non-IID Datacited by 1,108
  9. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  10. 2018LEAF: A Benchmark for Federated Settingscited by 792
  11. 2018How To Backdoor Federated Learningcited by 765
  12. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
18 more
  1. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  2. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  3. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  4. 2021Towards Personalized Federated Learningcited by 464
  5. 2020Backdoor Learning: A Surveycited by 426
  6. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 309
  7. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 283
  8. 2019Can You Really Backdoor Federated Learning?cited by 255
  9. 2018Mitigating Sybils in Federated Learning Poisoningcited by 161
  10. 2022FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clientscited by 125
  11. 2021CRFL: Certifiably Robust Federated Learning against Backdoor Attackscited by 91
  12. 2020Learning to Detect Malicious Clients for Robust Federated Learningcited by 88
  13. 2021Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learningcited by 86
  14. 2020Defending against Backdoors in Federated Learning with Robust Learning Ratecited by 82
  15. 2022DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspectioncited by 81
  16. 2021Provably Secure Federated Learning against Malicious Clientscited by 63
  17. 2022FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learningcited by 37
  18. 2021Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learningcited by 21

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 3; 2026: 1.

6 papers.

  1. 2023Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verificationcited by 2
  2. 2025BackFed: An Efficient & Standardized Benchmark Suite for Backdoor Attacks in Federated Learningcited by 1
  3. 2026FedSurrogate: Backdoor Defense in Federated Learning via Layer Criticality and Surrogate Replacementcited by 1
  4. 2024FLAIN: Mitigating Backdoor Attacks in Federated Learning via Flipping Weight Updates of Low-Activation Input Neuronscited by 0
  5. 2025FedBAP: Backdoor Defense via Benign Adversarial Perturbation in Federated Learningcited by 0
  6. 2025BAPFL: Exploring Backdoor Attacks Against Prototype-based Federated Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023On the Vulnerability of Backdoor Defenses for Federated Learning
  2. 2024Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning
  3. 2025Enhancing the Effectiveness and Durability of Backdoor Attacks in Federated Learning through Maximizing Task Distinction
  4. 2026Mitigating Backdoor Attacks in Federated Learning Using PPA and MiniMax Game Theory
  5. 2025TrojanDam: Detection-Free Backdoor Defense in Federated Learning through Proactive Model Robustification utilizing OOD Data
  6. 2022Invariant Aggregator for Defending against Federated Backdoor Attacks
  7. 2024DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning
  8. 2025SPA: Towards More Stealth and Persistent Backdoor Attacks in Federated Learning