mlsanityData to 7 Oct 2026

Fake or Compromised? Making Sense of Malicious Clients in Federated Learning

arXiv 2403.06319 ↗First posted 10 Mar 2024cs.LG cs.CRcited by 2

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Abstract (arXiv)

Federated learning (FL) is a distributed machine learning paradigm that enables training models on decentralized data. The field of FL security against poisoning attacks is plagued with confusion due to the proliferation of research that makes different assumptions about the capabilities of adversaries and the adversary models they operate under. Our work aims to clarify this confusion by presenting a comprehensive analysis of the various poisoning attacks and defensive aggregation rules (AGRs) proposed in the literature, and connecting them under a common framework. To connect existing adversary models, we present a hybrid adversary model, which lies in the middle of the spectrum of adversaries, where the adversary compromises a few clients, trains a generative (e.g., DDPM) model with their compromised samples, and generates new synthetic data to solve an optimization for a stronger (e.g., cheaper, more practical) attack against different robust aggregation rules. By presenting the spectrum of FL adversaries, we aim to provide practitioners and researchers with a clear understanding of the different types of threats they need to consider when designing FL systems, and identify areas where further research is needed.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 32 of its 42 references that are in the map. First posted on arXiv 10 Mar 2024.

Builds on

32 of 42 references.

References · 32

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Denoising Diffusion Implicit Modelscited by 9,655
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  4. 2022Classifier-Free Diffusion Guidancecited by 5,717
  5. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  6. 2019Advances and Open Problems in Federated Learningcited by 3,021
  7. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 2,994
  8. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  9. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  10. 2020Adaptive Federated Optimizationcited by 1,036
  11. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  12. 2018LEAF: A Benchmark for Federated Settingscited by 780
20 more
  1. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
  2. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 479
  3. 2022Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioningcited by 362
  4. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
  5. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 324
  6. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 307
  7. 2022Perception Prioritized Training of Diffusion Modelscited by 301
  8. 2019A Little Is Enough: Circumventing Defenses For Distributed Learningcited by 285
  9. 2019Can You Really Backdoor Federated Learning?cited by 250
  10. 2022Scalable Adaptive Computation for Iterative Generationcited by 190
  11. 2020Mime: Mimicking Centralized Stochastic Algorithms in Federated Learningcited by 152
  12. 2019Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfercited by 91
  13. 2021Federated Evaluation and Tuning for On-Device Personalization: System Design & Applicationscited by 84
  14. 2022MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clientscited by 65
  15. 2020Free-rider Attacks on Model Aggregation in Federated Learningcited by 57
  16. 2020IBM Federated Learning: an Enterprise Framework White Paper V0.1cited by 57
  17. 2019Free-riders in Federated Learning: Attacks and Defensescited by 48
  18. 2019Micro-Batch Training with Batch-Channel Normalization and Weight Standardizationcited by 45
  19. 2022No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objectscited by 26
  20. 2022FedPerm: Private and Robust Federated Learning by Parameter Permutationcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?cited by 3
  2. 2026Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunitiescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026PCDM: A Diffusion-Based Data Poisoning Attack Against Federated Learning Systems
  2. 2023TrustFed: A Reliable Federated Learning Framework with Malicious-Attack Resistance
  3. 2021Robust Federated Learning by Mixture of Experts
  4. 2026Generative AI and Federated Learning for Intrusion Detection Systems: A Survey
  5. 2026FedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching Generation
  6. 2025Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation
  7. 2025SABRE-FL: Selective and Accurate Backdoor Rejection for Federated Prompt Learning
  8. 2021Personalised Federated Learning: A Combinational Approach