mlsanityData to 14 Oct 2026

Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunities

arXiv 2609.28722 ↗First posted 23 Sep 2026cs.LGcited by 0

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Abstract (arXiv)

While Federated Learning (FL) has been widely adopted for protecting user privacy in machine learning, it remains vulnerable to various robustness challenges, including performance-impairment risks, information-stealing threats, and aggregation vulnerabilities. This work offers a holistic synthesis of FL robustness along three tightly coupled angles: (i) a threat-centric view of robustness that categorizes the multifaceted attack surfaces, (ii) a structured taxonomy of robust aggregation strategies distinguishing outcome-centric approaches from security-centric strategies, and (iii) a layered taxonomy of defensive strategies. We rigorously examine current evaluation practices for FL robustness and identify major applications and open research challenges to guide future research.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 84 of its 167 references that are in the map. First posted on arXiv 23 Sep 2026.

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84 of 167 references.

References · 84

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  3. 2016Adversarial Machine Learning at Scalecited by 936
  4. 2018How To Backdoor Federated Learningcited by 765
  5. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  6. 2017ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Modelscited by 666
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38 more
  1. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 346
  2. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 309
  3. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 275
  4. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 269
  5. 2019Can You Really Backdoor Federated Learning?cited by 255
  6. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 243
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  11. 2018RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasetscited by 149
  12. 2019Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulationcited by 141
  13. 2021Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learningcited by 118
  14. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 99
  15. 2022MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clientscited by 68
  16. 2020Data Poisoning Attacks on Federated Machine Learningcited by 62
  17. 2020FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learningcited by 61
  18. 2020Free-rider Attacks on Model Aggregation in Federated Learningcited by 57
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  20. 2021Quantum Federated Learning with Quantum Datacited by 53
  21. 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 51
  22. 2021Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryptioncited by 48
  23. 2021Cluster-driven Graph Federated Learning over Multiple Domainscited by 44
  24. 2020Dynamic Fusion based Federated Learning for COVID-19 Detectioncited by 36
  25. 2021FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspectivecited by 36
  26. 2021Source Inference Attacks in Federated Learningcited by 34
  27. 2020When Federated Learning Meets Blockchain: A New Distributed Learning Paradigmcited by 31
  28. 2024Membership Inference Attacks and Defenses in Federated Learning: A Surveycited by 31
  29. 2020Backdoor attacks and defenses in feature-partitioned collaborative learningcited by 30
  30. 2022FLCert: Provably Secure Federated Learning against Poisoning Attackscited by 30
  31. 2022Energy-Latency Attacks via Sponge Poisoningcited by 29
  32. 2022Privacy-Preserving Aggregation in Federated Learning: A Surveycited by 27
  33. 2021Time-Correlated Sparsification for Communication-Efficient Federated Learningcited by 26
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  36. 2022Federated Learning with Quantum Secure Aggregationcited by 21
  37. 2022FedRecAttack: Model Poisoning Attack to Federated Recommendationcited by 20
  38. 2019Man-in-the-Middle Attacks against Machine Learning Classifiers via Malicious Generative Modelscited by 19

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