mlsanityData to 14 Oct 2026

Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning

arXiv 2506.09674 ↗First posted 11 Jun 2025cs.LGcited by 2

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Abstract (arXiv)

Federated Learning (FL) enables the training of machine learning models across decentralized clients while preserving data privacy. However, the presence of anomalous or corrupted clients - such as those with faulty sensors or non representative data distributions - can significantly degrade model performance. Detecting such clients without accessing raw data remains a key challenge. We propose WAFFLE (Wavelet and Fourier representations for Federated Learning) a detection algorithm that labels malicious clients it before training, using locally computed compressed representations derived from either the Wavelet Scattering Transform (WST) or the Fourier Transform. Both approaches provide low-dimensional, task-agnostic embeddings suitable for unsupervised client separation. A lightweight detector, trained on a distillated public dataset, performs the labeling with minimal communication and computational overhead. While both transforms enable effective detection, WST offers theoretical advantages, such as non-invertibility and stability to local deformations, that make it particularly well-suited to federated scenarios. Experiments on benchmark datasets show that our method improves detection accuracy and downstream classification performance compared to existing FL anomaly detection algorithms, validating its effectiveness as a pre-training alternative to online detection strategies.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 31 of its 64 references that are in the map. First posted on arXiv 11 Jun 2025.

Builds on

31 of 64 references.

References · 31

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  5. 2019Towards Federated Learning at Scale: System Designcited by 832
  6. 2018LEAF: A Benchmark for Federated Settingscited by 792
  7. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  8. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 682
  9. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 346
  10. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 329
  11. 2018Dataset Distillationcited by 230
  12. 2018Mitigating Sybils in Federated Learning Poisoningcited by 161
19 more
  1. 2021Federated Learning for Open Bankingcited by 141
  2. 2022FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clientscited by 125
  3. 2020Learning to Detect Malicious Clients for Robust Federated Learningcited by 88
  4. 2021FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learningcited by 69
  5. 2018Kymatio: Scattering Transforms in Pythoncited by 58
  6. 2013Deep Scattering Spectrumcited by 52
  7. 2014Deep Roto-Translation Scattering for Object Classificationcited by 50
  8. 2023FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learningcited by 27
  9. 2021RoFL: Robustness of Secure Federated Learningcited by 25
  10. 2024Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspectivecited by 24
  11. 2021Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directionscited by 22
  12. 2023Optimizing the Collaboration Structure in Cross-Silo Federated Learningcited by 19
  13. 2022Geometric Scattering on Measure Spacescited by 17
  14. 2024FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preferencecited by 16
  15. 2018Compressing the Input for CNNs with the First-Order Scattering Transformcited by 11
  16. 2024RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregationcited by 10
  17. 2022Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated Learningcited by 7
  18. 2024Covariances for Free: Exploiting Mean Distributions for Training-free Federated Learningcited by 4
  19. 2024WhaleNet: a Novel Deep Learning Architecture for Marine Mammals Vocalizations on Watkins Marine Mammal Sound Databasecited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026RIPPLE in Still Water: Zero-Shot Clustering in Federated Learning with Wavelet Scattering Transformcited by 0
  2. 2026WaSA: Wavelet Scattering Autoencoders for Unsupervised Offline Detection of Malicious Clients in Federated Learningcited by 0