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

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 nonrepresentative data distributions—can significantly degrade model performance. Detecting such clients without accessing raw data remains a key challenge. We propose wavelet and fourier representations for FL), a detection algorithm that labels malicious clients before training, using locally computed compressed representations derived from either the wavelet scattering transform (WST) or the Fourier transform (FT). Both approaches provide low-dimensional, task-agnostic embeddings suitable for unsupervised client separation. A lightweight detector, trained on a distilled public dataset, performs the labeling with minimal communication and computational overhead. While both transforms enable effective detection, WST offers theoretical advantages, such as noninvertibility and stability to local deformations, that make it particularly well-suited to federated scenarios. Experiments on benchmark datasets demonstrate that our method improves both detection accuracy and downstream classification performance compared to existing FL anomaly detection algorithms, validating its effectiveness as an offline alternative to online detection strategies. Source code for this article is publicly available at https://github.com/davedleo/Waffle