federated learning

Federated Bayesian Surveillance of Mechanical Thrombectomy Adverse Events: A Population Risk Layer for Surgical Digital Twins

A federated Bayesian protocol for learning population-scale adverse-event surveillance as a distinct belief layer of the surgical digital twin is proposed, and a federated Bayesian protocol for learning it under formal privacy guarantees is evaluated.

Quantifying the Privacy Posture of Operator-Side 5G/O-RAN Profiles

This work quantifies privacy posture with k-anonymity, l-diversity and t-closeness, aggregate them into a composite Privacy-Posture Index (PPI), and measures residual re-identification across eight transformation configurations on internal PCAP captures and the public Idaho Labs 5GAD corpus.

TwinFedSeg: Anatomical Twin State-Guided Federated Learning for Multimodal Medical Image Segmentation.

TwinFedSeg, a federated learning framework guided by an anatomical twin state for multimodal medical image segmentation, is proposed, which consistently outperforms representative federated methods.

MediNet: Simplifying Federated and Privacy-Preserving AI Deployment in Healthcare.

MediNet is a comprehensive server-client Federated learning (FL) platform that allows hospitals and research centers to train Machine Learning (ML) and DL models without moving or exposing sensitive data.

FedDermaSeg: Federated Learning for Dermatological Image Segmentation

Skin cancer is a major global health concern, and early detection and accurate lesion delineation are important for effective diagnosis and treatment planning. Automated skin lesion analysis can assist dermatologists, with lesion segmentation serving as a fundamental step in computer-aided diagnostic systems. Conventio...

Tram-FL: Reducing Communication and Computation Costs through Sequential Model Circulation in Decentralized Federated Learning

Conventional decentralized federated learning (DFL) often focuses on clients, with each client maintaining a model copy, performing updates individually, and undertaking model exchange and integration. While fully leveraging computational resources can shorten training times, it can also lead to significant computation...

Blockchain enabled federated learning for privacy preserving medical data processing in healthcare as a systematic literature review

A PRISMA-based Systematic Literature Review was conducted to synthesize evidence from 37 peer-reviewed studies focusing on blockchain-enabled federated learning for healthcare data privacy, security, and preservation, indicating that the integration of blockchain and federated learning offers significant potential for...

The complexities and guises of anonymity: privacy-enhancing technologies and medical AI

As investment in artificial intelligence (AI) grows, so too do debates about how to enable the secondary use of patient data to train AI models. Privacy-enhancing technologies (PETs) such as synthetic data, federated learning, and Secure Data Environments (SDEs) have been put forward as technical solutions to facilitat...

Blockchain-Anchored Reputation for Robust Aggregation in Homomorphically Encrypted Federated Learning — Code and Data

Blockchain-Anchored Reputation for Robust Aggregation in Homomorphically Encrypted Federated LearningCode, data splits, results and figures. Changes in version 1.1.0 Added code/tcga/, the preprocessing, audit, baseline and robustness scripts for the second corpus, and nibabel to requirements.txt. Added the second-corpu...

AFHAL: Adaptive Federated Hybrid-Attention Learning for privacy-aware medical image segmentation

CESFL-ECG: communication-efficient split federated learning for patient-independent ECG classification

Code, per-run results, tables and figures for the manuscript "Communication-efficient split federated learning for patient-independent ECG classification". CESFL-ECG quantizes the smashed data and cut-layer gradients of split federated learning to 4 bits with stochastic rounding and is evaluated on MIT-BIH (DS1/DS2, fi...

DeadOrSlow: silence-aware client failure detection for semi-synchronous federated learning (code, raw logs and design log)

Reproducibility artifact for the manuscript "Dead, Hung, or Just Unreachable? Silence-Aware Client Failure Detection for Semi-Synchronous Federated Learning" (Z. Kutlu). Contents: the detector code shared by the online FL server and the offline trace replay; the FL server and Raspberry Pi client; the fault and network-...