mlsanityData to 14 Oct 2026

Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms

arXiv 2207.02337 ↗First posted 5 Jul 2022cs.LG cs.AI cs.CRcited by 4

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Abstract (arXiv)

The advent of federated learning has facilitated large-scale data exchange amongst machine learning models while maintaining privacy. Despite its brief history, federated learning is rapidly evolving to make wider use more practical. One of the most significant advancements in this domain is the incorporation of transfer learning into federated learning, which overcomes fundamental constraints of primary federated learning, particularly in terms of security. This chapter performs a comprehensive survey on the intersection of federated and transfer learning from a security point of view. The main goal of this study is to uncover potential vulnerabilities and defense mechanisms that might compromise the privacy and performance of systems that use federated and transfer learning.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 52 of its 121 references that are in the map. First posted on arXiv 5 Jul 2022.

Builds on

52 of 121 references.

References · 52

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2016Deep Learning with Differential Privacycited by 2,728
  4. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  5. 2019Deep Leakage from Gradientscited by 1,021
  6. 2018How To Backdoor Federated Learningcited by 765
  7. 2017Federated Multi-Task Learningcited by 672
  8. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  9. 2019Adversarial Training for Free!cited by 643
  10. 2018Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networkscited by 580
  11. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 520
  12. 2018ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Modelscited by 498
38 more
  1. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 493
  2. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 459
  3. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 459
  4. 2018Analyzing Federated Learning through an Adversarial Lenscited by 351
  5. 2016Semi-supervised Knowledge Transfer for Deep Learning from Private Training Datacited by 348
  6. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 334
  7. 2019Robust Aggregation for Federated Learningcited by 317
  8. 2018Feature Denoising for Improving Adversarial Robustnesscited by 314
  9. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 309
  10. 2018Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learningcited by 225
  11. 2019MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examplescited by 197
  12. 2018Machine Learning with Membership Privacy using Adversarial Regularizationcited by 186
  13. 2019Adversarial Training and Robustness for Multiple Perturbationscited by 183
  14. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  15. 2020Threats to Federated Learning: A Surveycited by 178
  16. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 176
  17. 2020Privacy and Robustness in Federated Learning: Attacks and Defensescited by 166
  18. 2018Expanding the Reach of Federated Learning by Reducing Client Resource Requirementscited by 163
  19. 2018Mitigating Sybils in Federated Learning Poisoningcited by 161
  20. 2018cpSGD: Communication-efficient and differentially-private distributed SGDcited by 158
  21. 2017Neural Trojanscited by 153
  22. 2020Learning to Detect Malicious Clients for Robust Federated Learningcited by 88
  23. 2019Data Poisoning against Differentially-Private Learners: Attacks and Defensescited by 78
  24. 2018Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applicationscited by 67
  25. 2020Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Modelscited by 44
  26. 2020A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learningcited by 41
  27. 2019Abnormal Client Behavior Detection in Federated Learningcited by 40
  28. 2019Attack-Resistant Federated Learning with Residual-based Reweightingcited by 40
  29. 2020Federated Model Distillation with Noise-Free Differential Privacycited by 40
  30. 2019Robust Federated Learning with Noisy Communicationcited by 34
  31. 2020Federated Transfer Learning: concept and applicationscited by 32
  32. 2019Secure and Efficient Federated Transfer Learningcited by 21
  33. 2020Privacy Analysis of Deep Learning in the Wild: Membership Inference Attacks against Transfer Learningcited by 20
  34. 2020Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcarecited by 18
  35. 2019A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learningcited by 15
  36. 2020How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learningcited by 12
  37. 2019Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustnesscited by 9
  38. 2019A Survey of Moving Target Defenses for Network Securitycited by 6

50 most cited of 52 All 52 (JSON)

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Cited by year

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

4 papers.

  1. 2024Decentralized Federated Learning: A Survey on Security and Privacycited by 25
  2. 2023A Recent Survey of Heterogeneous Transfer Learningcited by 8
  3. 2024Online Transfer Learning for RSV Case Detectioncited by 1
  4. 2024Survey of Privacy Threats and Countermeasures in Federated Learningcited by 0