federated learning

Improving diagnosis and interpretability in chest X-rays through federated learning and adversarial training

Chest X-ray imaging is the most common radiological test for diagnosing a wide range of lung conditions. Deep Learning (DL) has emerged as a powerful tool for automated analysis of chest X-rays, but it requires large, well-annotated datasets that are often difficult to obtain due to privacy concerns and instituti...

Byzantine-Robust Federated Learning

Constraint-Guided Parameter-Efficient Fine-Tuning Personalized Federated Learning with Adaptive Gaussian Differential Privacy and Conformal Calibration

FLInterrupt: An interactive federated learning simulator for client interruption experiments

We introduce FLInterrupt, an open-source, browser-based federated learning (FL) simulator for research on involuntary client interruption during synchronous FedAvg. The backend runs communication rounds in PyTorch. A React dashboard lets researchers set datasets and models, interrupt or reconnect clients at runtime, re...

Practical Byzantine-Resilient Federated Learning via Zero-Knowledge Proofs and Adaptive Reputation

Trust management under adversarial uncertainty is a central challenge in distributed learning systems. We propose Verifiable FL with Two-Stage Selection (VFL-TS), a knowledge-driven framework for Byzantine-resilient federated learning that maintains a dynamic trust knowledge base—updated through cryptographically verif...

Lifelong-IDS

Federated Learning (FL) enables collaborative intrusion detection across distributed Internet of Things (IoT) edge devices without centralizing private network telemetry. However, existing frameworks assume static data distributions and fail under non-stationary conditions: emerging zero-day attack families cause sever...

Privacy-Preserving Federated Remote Sensing via Spatially Modulated Feature Perturbation and Ground Alignment

Space-to-ground collaborative remote sensing has become a promising mode for global-scale earth observation. However, the open satellite-ground communication link faces severe security threats, especially when unauthorized eavesdroppers deploy gradient or feature inversion attacks based on deep learning to reconstruct...

AI-Driven Learning Analytics for Personalized STEAM Education

The AI-based learning analytics is revolutionizing STEAM learning by facilitating adaptable, information-based, and individualised learning space. The chapter discusses the application of machine learning, natural language processing and predictive analytics to multimodal data on learners to create dynamic learner prof...

Getting Essential Knowledge: Disentanglement Learning-driven Federated Domain Generalization

THE EL-RAKHAWI ARCHITECTURE FOR PREDICTIVE BIOSENSING AND PRECISION THERAPEUTICS (EAPBPT) A Mathematically Provable, Clinically Validated Framework for Pre-Symptomatic Disease Detection, AI-Driven Diagnosis, and Pharmacogenomically Optimized Treatment

The El-Rakhawi Architecture for Predictive Biosensing and Precision Therapeutics (EAPBPT) by Dr. Mohamed Kamal Arafa El-Rakhawi (DOI: 10.5281/zenodo.23084200) integrates six validated technologies for pre-symptomatic disease detection and personalized treatment: multi-omic liquid biopsy, lab-on-a-chip microfluidics, we...

Federated Learning in Healthcare Data Analytics: A Privacy-Preserving Approach

The rapid digitization of healthcare has led to an explosion of patient data, necessitating advanced analytics for improved diagnostics, treatment, and predictive modeling. However, traditional centralized data processing poses significant privacy and security risks, particularly concerning sensitive patient informatio...

Athena Techne SecureFL: Byzantine-Resilient Federated Learning