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Security and Privacy Issues and Solutions in Federated Learning for Digital Healthcare

arXiv 2401.08458 ↗First posted 16 Jan 2024cs.CR cs.AIcited by 0

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Abstract (arXiv)

The advent of Federated Learning has enabled the creation of a high-performing model as if it had been trained on a considerable amount of data. A multitude of participants and a server cooperatively train a model without the need for data disclosure or collection. The healthcare industry, where security and privacy are paramount, can substantially benefit from this new learning paradigm, as data collection is no longer feasible due to stringent data policies. Nonetheless, unaddressed challenges and insufficient attack mitigation are hampering its adoption. Attack surfaces differ from traditional centralized learning in that the server and clients communicate between each round of training. In this paper, we thus present vulnerabilities, attacks, and defenses based on the widened attack surfaces, as well as suggest promising new research directions toward a more robust FL.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 30 of its 60 references that are in the map. First posted on arXiv 16 Jan 2024.

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30 of 60 references.

References · 30

  1. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  3. 2018How To Backdoor Federated Learningcited by 745
  4. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  5. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
  6. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 509
  7. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 479
  8. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  9. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
  10. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 330
  11. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 324
  12. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 307
18 more
  1. 2019A Little Is Enough: Circumventing Defenses For Distributed Learningcited by 285
  2. 2019Can You Really Backdoor Federated Learning?cited by 250
  3. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 242
  4. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 219
  5. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 208
  6. 2021FedKD: Communication Efficient Federated Learning via Knowledge Distillationcited by 185
  7. 2022Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learningcited by 172
  8. 2020Threats to Federated Learning: A Surveycited by 171
  9. 2020Privacy and Robustness in Federated Learning: Attacks and Defensescited by 163
  10. 2020Federated Learning with Compression: Unified Analysis and Sharp Guaranteescited by 159
  11. 2021PPFL: Privacy-preserving Federated Learning with Trusted Execution Environmentscited by 93
  12. 2019Data Poisoning against Differentially-Private Learners: Attacks and Defensescited by 75
  13. 2020Free-rider Attacks on Model Aggregation in Federated Learningcited by 57
  14. 2019Free-riders in Federated Learning: Attacks and Defensescited by 48
  15. 2020Mitigating Backdoor Attacks in Federated Learningcited by 39
  16. 2020Fairness and Accuracy in Federated Learningcited by 27
  17. 2020Two-Phase Multi-Party Computation Enabled Privacy-Preserving Federated Learningcited by 19
  18. 2021Property Inference Attacks on Convolutional Neural Networks: Influence and Implications of Target Model's Complexitycited by 19

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