mlsanityData to 14 Oct 2026

FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs

arXiv 2306.04959 ↗First posted 8 Jun 2023cs.CR cs.AIcited by 21

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Abstract (arXiv)

This paper introduces FedSecurity, an end-to-end benchmark that serves as a supplementary component of the FedML library for simulating adversarial attacks and corresponding defense mechanisms in Federated Learning (FL). FedSecurity eliminates the need for implementing the fundamental FL procedures, e.g., FL training and data loading, from scratch, thus enables users to focus on developing their own attack and defense strategies. It contains two key components, including FedAttacker that conducts a variety of attacks during FL training, and FedDefender that implements defensive mechanisms to counteract these attacks. FedSecurity has the following features: i) It offers extensive customization options to accommodate a broad range of machine learning models (e.g., Logistic Regression, ResNet, and GAN) and FL optimizers (e.g., FedAVG, FedOPT, and FedNOVA); ii) it enables exploring the effectiveness of attacks and defenses across different datasets and models; and iii) it supports flexible configuration and customization through a configuration file and some APIs. We further demonstrate FedSecurity's utility and adaptability through federated training of Large Language Models (LLMs) to showcase its potential on a wide range of complex applications.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 59 of its 107 references that are in the map. First posted on arXiv 8 Jun 2023.

Builds on

59 of 107 references.

References · 59

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38 more
  1. 2018How To Backdoor Federated Learningcited by 765
  2. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  3. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  4. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
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  6. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 520
  7. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 493
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  12. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
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  14. 2019Can You Really Backdoor Federated Learning?cited by 255
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  16. 2019Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulationcited by 141
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  19. 2020Byzantine-Robust Learning on Heterogeneous Datasets via Bucketingcited by 128
  20. 2019Federated Learning for Emoji Prediction in a Mobile Keyboardcited by 118
  21. 2021Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Modelscited by 108
  22. 2020Feature Inference Attack on Model Predictions in Vertical Federated Learningcited by 106
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  31. 2020Free-rider Attacks on Model Aggregation in Federated Learningcited by 57
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  34. 2019Free-riders in Federated Learning: Attacks and Defensescited by 49
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  38. 2022LoMar: A Local Defense Against Poisoning Attack on Federated Learningcited by 31

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 6; 2025: 12; 2026: 3.

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  1. 2024Large Language Model Safety: A Holistic Surveycited by 59
  2. 2025From Prompt Injections to Protocol Exploits: Threats in LLM-Powered AI Agents Workflowscited by 58
  3. 2024Federated Large Language Models: Current Progress and Future Directionscited by 27
  4. 2025Ten Challenging Problems in Federated Foundation Modelscited by 20
  5. 2025FedMentalCare: Towards Privacy-Preserving Fine-Tuned LLMs to Analyze Mental Health Status Using Federated Learning Frameworkcited by 8
  6. 2026Revisiting Backdoor Threat in Federated Instruction Tuning from a Signal Aggregation Perspectivecited by 5
  7. 2026Rethinking LoRA for Privacy-Preserving Federated Learning in Large Modelscited by 5
  8. 2024FATS: A Prompt Injection Attack Utilizing Feign Security Agents with Deceptive Few-shots Learningcited by 4
  9. 2025Graph Representation-based Model Poisoning on the Heterogeneous Internet of Agentscited by 3
  10. 2025Communication-Efficient and Personalized Federated Foundation Model Fine-Tuning via Tri-Matrix Adaptationcited by 2
  11. 2025Blockchain-Enabled Privacy-Preserving Second-Order Federated Edge Learning in Personalized Healthcarecited by 2
  12. 2024FedCAP: Robust Federated Learning via Customized Aggregation and Personalizationcited by 1
9 more
  1. 2024Bridging Today and the Future of Humanity: AI Safety in 2024 and Beyondcited by 1
  2. 2025Poison Once, Refuse Forever: Weaponizing Alignment for Injecting Bias in LLMscited by 1
  3. 2025FedPoP: Federated Learning Meets Proof of Participationcited by 1
  4. 2026Graph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMscited by 1
  5. 2024Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated Learningcited by 0
  6. 2025Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updatescited by 0
  7. 2025Client Clustering Meets Knowledge Sharing: Enhancing Privacy and Robustness in Personalized Peer-to-Peer Learningcited by 0
  8. 2025On the Evolution of Federated Post-Training Large Language Models: A Model Accessibility Viewcited by 0
  9. 2025Bi-level Personalization for Federated Foundation Models: A Task-vector Aggregation Approachcited by 0