Wavelet Scattering Transforms

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9 papers4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

WaSA: Wavelet Scattering Autoencoders for Unsupervised Offline Detection of Malicious Clients in Federated Learning
Oct 9, 2026Alessandro LicciardiWavelet Scattering Transforms Byzantine-Robust Federated Learning
Department of Mathematical Sciences, Politecnico di Torino, Turin, IT · National Institute of Nuclear Physics (INFN), Sezione di Torino, Turin, IT
Specific Algorithmic Interpretability of Neural Networks: A Case Study on Textures
Oct 3, 2026Yanglin Zhang, Anneke von Seeger, Gilad Lerman +1Wavelet Scattering Transforms Neural Network Interpretability
Technion – Israel Institute of Technology, Faculty of Mathematics · University of Minnesota, School of Mathematics
RIPPLE in Still Water: Zero-Shot Clustering in Federated Learning with Wavelet Scattering Transform
Oct 2, 2026Alessandro LicciardiWavelet Scattering Transforms Clustered Federated Learning
Department of Mathematical Sciences Politecnico di Torino, Turin, Italy
WST-Graph: Topology-Preserving Wavelet Scattering Front-End for Speech Deepfake Detection
Sep 24, 2026Kwok-Ho Ng, Tingting Song, Bingwen Feng +1Audio Deepfake Detection Wavelet Methods
College of Cyber Security, Jinan University, Guangzhou, China
Wavelet Scattering Transform for Interpretable Schizophrenia Biomarker Discovery and Classification from Resting-State EEG
Jul 6, 2026Md. Taksimul Ahsan Tawhid, Nasif Ahmed Rafe, Alif Tahmid Priyom +1Electroencephalography EEG Decoding
Department of EECE, Military Institute of Science and Technology (MIST), Mirpur Cantonment, Dhaka, 1216, Bangladesh
Masked Wavelet Scattering Transform Neural Field for Sound Field Reconstruction
Jun 3, 2026Xinmeng Luan, Samuel A. Verburg, Efren Fernandez-Grande +1Wavelet Scattering Transforms
Schulich School of Music, McGill University, CIRMMT† Montreal, Canada · Dept. of Electrical and Photonics Engineering, Technical University of Denmark Kgs. Lyngby, Denmark · Dept. of Audiovisual Engineering and Communications, Universidad Politécnica de Madrid Madrid, Spain
Phase-Aware Wavelet-Based-Scattering Encoder-Decoder for Dense Predictions
May 23, 2026Ghassen Marrakchi, Basarab MateiWavelet Methods Wavelet Scattering Transforms
Northern Paris Computer Science Lab, Sorbonne Paris Nord University, Villetaneuse, France.
Scattering Transform for Auditory Attention Decoding
Feb 26, 2026René Pallenberg, Fabrice Katzberg, Alfred Mertins +1Wavelet Scattering Transforms EEG Decoding
Institute for Signal Processing, University of Luebeck, 23562 Luebeck, Germany · Department for Vision and Machine Learning, FPI Food Processing Innovation GmbH & Co. KG, 23556 Luebeck, Germany · German Research Center for Artificial Intelligence (DFKI), AI for Assistive Health Technologies, 23562 Luebeck, Germany
WaveScat: Wavelet Scattering Front-Ends with Self-Supervised Features for Speech Deepfake Detection
Feb 3, 2026Xi Xuan, Davide Carbone, Wenxin Zhang +1Wavelet Scattering Transforms Audio Deepfake Detection
Computational Speech Group at the University of Eastern Finland · Laboratoire de Physique de l’Ecole Normale Sup´erieure, Universit´e PSL, CNRS, Sorbonne Universit´e, Universit´e de Paris · School of Computer Science and Technology at the University of Chinese Academy of Sciences and the Department of Mathematics at the University of Toronto

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WaSA: Wavelet Scattering Autoencoders for Unsupervised Offline Detection of Malicious Clients in Federated Learning

Alessandro Licciardi

Department of Mathematical Sciences, Politecnico di Torino, Turin, IT · National Institute of Nuclear Physics (INFN), Sezione di Torino, Turin, IT

Wavelet Scattering Transforms Byzantine-Robust Federated Learning Unsupervised Anomaly Detection

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Federated Learning (FL) enables collaborative model training without exposing raw data, yet remains vulnerable to malicious clients injecting poisoned datasets. Existing defenses are typically reactive (operating during training) or supervised (requiring prior knowledge of attack types) - assumptions that rarely hold in open-ended IoT deployments. We propose WaSA, a fully unsupervised, offline framework for malicious client detection. Before training, each client transmits a single, privacy-preserving spectral summary of its data to the server. A denoising autoencoder, trained solely on clean auxiliary data, scores clients based on their deviation from the learned benign manifold, identifying corruptions regardless of the attack type. WaSA requires no attack labels, generalizes to unseen anomalies, and introduces a Boltzmann sampling strategy for client selection that smoothly interpolates between hard exclusion and standard aggregation. Experiments on MNIST, CIFAR-10, and CIFAR-100 under diverse attack scenarios (up to 90 % malicious clients) demonstrate consistent improvements over supervised baselines and robust aggregation methods, with negligible communication overhead. Code is available at https://github.com/alelicciardi99/wavelet_autoencoders_malicious.