mlsanityData to 14 Oct 2026

Exploring adversarial attacks in federated learning for medical imaging

arXiv 2310.06227 ↗First posted 10 Oct 2023cs.CR cs.LG eess.IVcited by 8

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Abstract (arXiv)

Federated learning offers a privacy-preserving framework for medical image analysis but exposes the system to adversarial attacks. This paper aims to evaluate the vulnerabilities of federated learning networks in medical image analysis against such attacks. Employing domain-specific MRI tumor and pathology imaging datasets, we assess the effectiveness of known threat scenarios in a federated learning environment. Our tests reveal that domain-specific configurations can increase the attacker's success rate significantly. The findings emphasize the urgent need for effective defense mechanisms and suggest a critical re-evaluation of current security protocols in federated medical image analysis systems.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 21 of its 42 references that are in the map. First posted on arXiv 10 Oct 2023.

Builds on

21 of 42 references.

References · 21

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  3. 2013Intriguing properties of neural networkscited by 5,155
  4. 2016Adversarial examples in the physical worldcited by 1,769
  5. 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,517
  6. 2017Evasion Attacks against Machine Learning at Test Timecited by 689
  7. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  8. 2018Rotation Equivariant CNNs for Digital Pathologycited by 422
  9. 2019Can You Really Backdoor Federated Learning?cited by 255
  10. 2019Adversarial Attacks and Defenses in Images, Graphs and Text: A Reviewcited by 237
  11. 2019Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systemscited by 153
  12. 2018Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defensescited by 109
9 more
  1. 2019Adversarial Robustness vs Model Compression, or Both?cited by 98
  2. 2021Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learningcited by 39
  3. 2023Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directionscited by 33
  4. 2018Distributionally Adversarial Attackcited by 26
  5. 2016Assessing Threat of Adversarial Examples on Deep Neural Networkscited by 9
  6. 2018A Bi-layered Parallel Training Architecture for Large-scale Convolutional Neural Networkscited by 6
  7. 2019Stochastic Channel-Based Federated Learning for Medical Data Privacy Preservingcited by 5
  8. 2023A Comparative Study of Federated Learning Models for COVID-19 Detectioncited by 3
  9. 2022AI Technical Considerations: Data Storage, Cloud usage and AI Pipelinecited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 5; 2026: 1.

8 papers.

  1. 2024Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challengescited by 15
  2. 2025FlexOlmo: Open Language Models for Flexible Data Usecited by 15
  3. 2023The Hidden Adversarial Vulnerabilities of Medical Federated Learningcited by 1
  4. 2025Accelerated Training of Federated Learning via Second-Order Methodscited by 0
  5. 2025Autonomous Cyber Resilience via a Co-Evolutionary Arms Race within a Fortified Digital Twin Sandboxcited by 0
  6. 2025OptiGradTrust: Byzantine-Robust Federated Learning with Multi-Feature Gradient Analysis and Reinforcement Learning-Based Trust Weightingcited by 0
  7. 2025Lightweight and Robust Federated Data Valuationcited by 0
  8. 2026MetaMoE: Diversity-Aware Proxy Selection for Privacy-Preserving Mixture-of-Experts Unificationcited by 0