mlsanityData to 7 Oct 2026

Trustworthy Federated Learning: Privacy, Security, and Beyond

arXiv 2411.01583 ↗First posted 3 Nov 2024cs.CR cs.AI cs.DCcited by 11

Not on the map. Why?

Abstract (arXiv)

While recent years have witnessed the advancement in big data and Artificial Intelligence (AI), it is of much importance to safeguard data privacy and security. As an innovative approach, Federated Learning (FL) addresses these concerns by facilitating collaborative model training across distributed data sources without transferring raw data. However, the challenges of robust security and privacy across decentralized networks catch significant attention in dealing with the distributed data in FL. In this paper, we conduct an extensive survey of the security and privacy issues prevalent in FL, underscoring the vulnerability of communication links and the potential for cyber threats. We delve into various defensive strategies to mitigate these risks, explore the applications of FL across different sectors, and propose research directions. We identify the intricate security challenges that arise within the FL frameworks, aiming to contribute to the development of secure and efficient FL systems.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 141 of its 262 references that are in the map. First posted on arXiv 3 Nov 2024.

Builds on

141 of 262 references.

References · 141

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  4. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  5. 2013Intriguing properties of neural networkscited by 5,114
  6. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  7. 2019Advances and Open Problems in Federated Learningcited by 3,021
  8. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  9. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
  10. 2020Extracting Training Data from Large Language Modelscited by 2,144
  11. 2016Adversarial examples in the physical worldcited by 1,762
  12. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
38 more
  1. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  2. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,315
  3. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  4. 2018Federated Learning with Non-IID Datacited by 1,099
  5. 2017BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chaincited by 1,052
  6. 2019Deep Leakage from Gradientscited by 1,004
  7. 2018The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networkscited by 847
  8. 2015The Limitations of Deep Learning in Adversarial Settingscited by 784
  9. 2018How To Backdoor Federated Learningcited by 745
  10. 2017Federated Multi-Task Learningcited by 669
  11. 2017ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Modelscited by 656
  12. 2020The Future of Digital Health with Federated Learningcited by 625
  13. 2017In-Datacenter Performance Analysis of a Tensor Processing Unitcited by 610
  14. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  15. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 509
  16. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  17. 2017Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descentcited by 496
  18. 2017Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networkscited by 484
  19. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 479
  20. 2022Diffusion Models for Adversarial Purificationcited by 469
  21. 2018Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Surveycited by 459
  22. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  23. 2016Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samplescited by 439
  24. 2018Detecting Backdoor Attacks on Deep Neural Networks by Activation Clusteringcited by 429
  25. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  26. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  27. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
  28. 2017Defense against Adversarial Attacks Using High-Level Representation Guided Denoisercited by 329
  29. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 326
  30. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 324
  31. 2018Feature Denoising for Improving Adversarial Robustnesscited by 312
  32. 2022Perception Prioritized Training of Diffusion Modelscited by 301
  33. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  34. 2021Recent Advances in Adversarial Training for Adversarial Robustnesscited by 276
  35. 2017MagNet: a Two-Pronged Defense against Adversarial Examplescited by 268
  36. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
  37. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 242
  38. 2018Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learningcited by 242

50 most cited of 141 All 141 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 9; 2026: 2.

11 papers.

  1. 2025Federated Knowledge Distillation for Multi-Model Architectures Lithography Hotspot Detectioncited by 8
  2. 2025SGDPO: Self-Guided Direct Preference Optimization for Language Model Alignmentcited by 3
  3. 2025Efficient Federated Learning with Heterogeneous Data and Adaptive Dropoutcited by 2
  4. 2025RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utilitycited by 1
  5. 2025Enhancing Federated Survival Analysis through Peer-Driven Client Reputation in Healthcarecited by 1
  6. 2025Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Thingscited by 1
  7. 2025Hybrid Reputation Aggregation: A Robust Defense Mechanism for Adversarial Federated Learning in 5G and Edge Network Environmentscited by 1
  8. 2026A Secure and Private Distributed Bayesian Federated Learning Designcited by 1
  9. 2025Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updatescited by 0
  10. 2025From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0cited by 0
  11. 2026A Differentially Private Federated Proximal Optimization Framework for Customer Churn Prediction in Heterogeneous Federated Telecom Networkscited by 0