federated learning

A POST-QUANTUM BLOCKCHAIN FRAMEWORK FOR CONNECTED SMART VEHICLES

This paper presents OmniGuard V2X, a security prototype for connected and au-tonomous vehicles. The system integrates blockchain auditing, zero-knowledge privacy, decentral-ized identity, post-quantum key establishment, federated learning, and an encrypted forensic black box into a unified architecture. Rather than mak...

ELASTIC Newsletter #5: Into the Final Stretch

The fifth issue of the ELASTIC project newsletter marks the project's final stretch towards its conclusion in February 2027, as the consortium moves from mid-term results to system-level integration and validation. This issue covers ELASTIC's participation at EuCNC & 6G Summit 2026 in Málaga (Booth 76), where both demo...

FedMobileNet: A Resource-Conscious Federated Learning Framework for Imbalanced Lung Cancer CT Image Classification

Federated learning enables distributed training without exchanging original images, but non-IID data and class imbalance can degrade model performance. In this study, FedMobileNet is presented as a decentralized machine learning system based on MobileNetV2 for multiclass classification. The training instances were spre...

Bayes-Federated Learning

Privacy-Preserving Network Intrusion Detection Using Federated Learning with Explainable AI and an Autonomous SOC Agent Pipeline

Centralized intrusion detection systems (IDS) re-quire pooling raw network traffic at a single location, which is a significant privacy liability in domains such as banking, healthcare, and telecommunications, and typically rely on static detection thresholds that produce high false-positive rates as traffic patterns d...

A Personalized Federated Autoencoder Framework for IoT Anomaly Detection and Policy-Mediated Firewall Enforcement

Edge protection of heterogeneous Internet of Things (IoT) devices needs per-device behavior models, cross-site learning, and blocking that spares legitimate services. This systems study presents the Personalized Federated Autoencoder Framework (PFAF), deployed on a Raspberry Pi access point. PFAF passively aggregates t...

Trustworthy Phishing URL Detection Using Machine Learning: Performance Analysis and Future Directions

Over the years, Uniform Resource Locator (URL) Phishing has been a threat to online security because their scams exploit trust in a misleading URL to trick unsuspecting users into disclosing their username or passwords and other personal information. Not only that, but it is also crucial to identify these malicious URL...

An employment tracking and career trajectory analysis system for university graduates

The employment market of college graduates is "double increase in supply and demand, competitive polarization" and the traditional employment service system has data islanding and insufficient dynamic tracking ability. This paper proposes a closed-loop employment tracking and career trajectory analysis system based on...

Uncertainty-Aware Deep Learning for Skin Cancer Diagnosis

Although recent models achieve high diagnostic accuracy, uncertainty reporting remains limited, uncertainty thresholds are rarely validated, calibration practices are inconsistent, and external clinical evaluation is scarce, the synthesis shows that although recent models achieve high diagnostic accuracy, uncertainty r...

TRUSTWORTHY ARTIFICIAL INTELLIGENCE FOR MEDICAL IMAGE ANALYSIS: A COMPREHENSIVE REVIEW OF ROBUST DEEP LEARNING TECHNIQUES FOR CLINICAL DECISION SUPPORT

Medical image analysis has been revolutionized by the advent of artificial intelligence, which has made automated disease detection, segmentation and diagnosis possible across a range of imaging modalities, thus supporting clinical decisions. However, the increasing complexity of deep learning models has raised concern...

Communication-Efficient and Robust Federated Training of Vision Models on IoT Camera Devices

Federated Spiking GANs with Rényi Differential Privacy for privacy-preserving synthetic EEG generation in brain–computer interfaces

Electroencephalography (EEG) data used in brain-computer and brain-machine interfaces (BCI/BMI) contain sensitive, user-specific information, creating privacy risks in consumer applications such as gaming, human augmentation, and virtual reality. Existing methods like encryption and anonymization either fail to protect...