Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning
This addresses the challenge of identifying anomalous clients in federated learning without accessing raw data, offering a pre-training detection alternative, though it appears incremental as it builds on existing anomaly detection algorithms.
The paper tackles the problem of detecting malicious clients in federated learning before training to prevent model degradation, proposing WAFFLE, which uses wavelet or Fourier transforms for compressed representations and improves detection accuracy and downstream performance compared to existing methods.
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.