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

• URL: http://arxiv.org/abs/2506.09674v1
• Date: Wed, 11 Jun 2025 12:48:00 GMT
• Title: Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning
• Authors: Alessandro Licciardi, Davide Leo, Davide Carbone,
• Abstract summary: Federated Learning (FL) enables the training of machine learning models across decentralized clients while preserving data privacy.<n>The presence of anomalous or corrupted clients can significantly degrade model performance.<n>We propose WAFFLE a detection algorithm that labels malicious clients it before training<n>A lightweight detector, trained on a distillated public dataset, performs the labeling with minimal communication and computational overhead.
• Score: 44.99833362998488
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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