Federated Self-Supervised Contrastive Learning for Privacy-Aware Domain Generalization in Deep Neural Networks

A Federated Deep Learning Framework for Privacy-Preserving Digital Identity Anomaly Detection in Distributed Networks

The increasing use of distributed digital services has created significant challenges in detecting identity anomalies while protecting sensitive user information. This study develops a federated deep learning framework for privacy-preserving digital identity anomaly detection in distributed networks. The research focus...

Privacy-Preserving Federated Learning Framework for Network Intrusion Detection

The findings demonstrate that the combination of TCN and FedProx provides an effective solution for privacy-preserving intrusion detection in heterogeneous federated environments.

PRIVACY-PRESERVING FEDERATED DEEP LEARNING FOR ZERO-DAY ATTACK DETECTION USING HYBRID CNN–LSTM NETWORKS

The expanding deployment of Internet of Things (IoT) and Industrial Internet of Things (IIoT) devices increases exposure to previously unseen cyberattacks, while centralized intrusion-detection training requires network records from different sites to be pooled. This paper presents a federated convolutional long short-...

Can federated models keep secrets better?

FedContrast-Shot: Privacy-Preserving Federated Contrastive Meta-Learning for Few-Shot Anomaly Detection in Cloud–Edge IIoT

Cloud-based deployments of Industrial Internet of Things (IIoT) systems have become more common for real-time monitoring and control of critical systems. However, anomaly detection in such environments remains challenging because labeled attack data are scarce, operational data are distributed across privacy-sensitive...

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

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...