← Back to all news
arXiv Machine Learning By Alessandro Licciardi

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

Read the original on arXiv Machine Learning →

WaSA is a fully unsupervised, offline framework that detects malicious clients in federated learning by having each client send a single privacy‑preserving spectral summary before training. A denoising autoencoder, trained only on clean data, scores clients based on deviation from a learned benign manifold, enabling detection of any attack type without prior labels. Experiments on MNIST, CIFAR‑10, and CIFAR‑100 with up to 90 % malicious clients show consistent improvements over supervised baselines and robust aggregation methods, with negligible communication overhead.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Sep 23

FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in Federated Learning

FedNIA is a defense framework for federated learning that identifies and excludes malicious clients without needing a central test dataset. It works by injecting random noise inputs and analyzing layerwise activation patterns with an autoencoder to detect abnormal behaviors caused by data poisoning. The method can counter various attack types—including sample poisoning, label flipping, and backdoors—even when multiple attackers collaborate, and shows strong performance on non‑iid federated datasets.

By Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif
safety
More like this →
Hugging Face Trending Papers
Jul 6

F-ACVAE: A Federated Adaptive Conditional Variational Auto-Encoder for Privacy-Preserving Intrusion Detection in IoT Networks

The rapid proliferation of Internet of things (IoT) devices has significantly expanded the cyber-attack surface, necessitating robust and privacy-preserving intrusion detection systems (IDS). However, centralized learning approaches often suffer from severe performance degradation due to high-dimensional traffic data, extreme class imbalance, and highly non-independent and identically distributed (non-IID) data across heterogeneous edge devices.

benchmarkssafety
More like this →
arXiv Machine Learning
Jul 7

F-ACVAE: A Federated Adaptive Conditional Variational Auto-Encoder for Privacy-Preserving Intrusion Detection in IoT Networks

arXiv:2607. 04698v1 Announce Type: new Abstract: The rapid proliferation of Internet of things (IoT) devices has significantly expanded the cyber-attack surface, necessitating robust and privacy-preserving intrusion detection systems (IDS).

By Mohammad Ansarimehr, Somayeh Changiz, Ehsan Baghishani, Ali Mousavi
benchmarkssafety
More like this →
arXiv AI
Aug 6

FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks

arXiv:2608. 04073v1 Announce Type: cross Abstract: Personalized Federated Learning (PFL) has emerged as a promising solution for intrusion detection in heterogeneous IoT environments, as it can improve local adaptation under highly Non-Independent and Identically Distributed (non-IID) data distributions.

By An Khanh Bui, Cong Thanh Nguyen, Hoang-Anh Pham, Hoang Thai Dinh, Diep N. Nguyen
reinforcement-learning
More like this →
arXiv AI
Sep 24

Backdoors Leave Structural Traces: FedMAST for Backdoor Detection and Containment in Federated Learning

The paper introduces FedMAST, a Federated Multi‑Axis Structural Tracing defense designed to detect and contain backdoor attacks in federated learning. FedMAST evaluates client updates through complementary structural, spectral, and historical evidence, applying tiered filtering and round‑level containment. In experiments across six backdoor attacks, FedMAST consistently achieves lower attack success rates while preserving high main‑task accuracy.

By Srinivasan Subramanian, Kazi Aminul Islam, Md. Abdullah Al Hafiz Khan
multimodalsafety
More like this →