federated learning

Diagnostic Performance of Deep Learning Models for Ocular Toxoplasmosis Using Fundus Photography: A Systematic Review and Meta-Analysis.

BACKGROUND Ocular toxoplasmosis (OT) is the leading infectious cause of posterior uveitis worldwide, yet diagnosis remains challenging without a universal gold standard. Although deep learning (DL) shows significant promise for automated image analysis, a rigorous synthesis of its diagnostic accuracy across heterogeneo...

Grid-constrained peer-to-peer energy trading: enforcement-mechanism selection, personalized federated forecasting and auditable energy-quantity settlement

Decentralized energy markets can coordinate distributed generation, flexible demand and storage, but feeder feasibility, household forecasting and smart-contract settlement are usually assessed separately. This study develops a grid-constrained Decentralized Energy Marketplace (DEM) linking household commitments, bilat...

Defending Against Cyber Threats in the Smart Healthcare Ecosystem

This book offers a practical guide to securing smart Internet of Things (IoT)-based healthcare devices against cyber threats through hands-on tools, threat modeling, risk scoring, and artificial intelligence (AI)-enhanced defenses. Healthcare is undergoing rapid digital transformation, with smart IoT devices from weara...

Securing AI Chatbots: A Framework for Trust and Standardization

AI-based chatbots have taken over industries such as health care, finance, and customer services. However, the implementation of AI chatbots poses many issues regarding trust, privacy, and proper ethical use of this technology. This paper discusses a framework to standardize trust and security in AI-based chatbots. The...

PCOS-FedSev: Federated severity-aware PCOS diagnosis with site-invariant disentanglement and conformal guarantees

Abstract Polycystic ovary syndrome (PCOS) is a condition that affects between 8 and 13% of women during their reproductive years. However, PCOS continues to be vastly underdiagnosed despite its prevalence. Currently available AI-based PCOS diagnosis methods are limited by their centralized nature, making them difficult...

Poisoning Federated Knowledge Graph Embeddings Under Noisy Conditions

Navigating the privacy-accuracy tradeoff: federated survival analysis with binning and differential privacy

Federated Learning (FL) offers a decentralized approach to model training, allowing for data-driven insights while safeguarding patient privacy across institutions. In the Personal Health Train (PHT) paradigm, it is the local model gradients from each institution, aggregated over a sample size of its own patients that...

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-...

Comparative Performance of Federated and Centralized Learning for Brain Tumor Classification and Segmentation

Purpose To compare federated learning (FL) and centralized learning (CL) performance for brain tumor classification and segmentation and examine whether specific configurations confer measurable performance advantages. Materials and Methods In this systematic review and meta-analysis, Medline, EMBASE, PubMed, Web of Sc...

A Privacy-Preserving Data Sharing Framework Driven by Blockchain for Smart Childcare Subsidy Systems

With the continuing development of digital government and public childcare-assistance programs, cross-departmental subsidy services must jointly address sensitive-data minimization, dynamic authorization, fraudulent-application identification, and accountable auditing. This paper proposes a blockchain-driven privacy-pr...

Federated Learning and Privacy-Preserving AI in Healthcare

The healthcare industry collects large volumes of sensitive information every day; however, it shares these data only to a limited extent because of regulatory requirements, competitive concerns, and other constraints. This makes it difficult to develop AI models for cybersecurity applications. The proposed solution is...

Personalized Federated Intrusion Detection for Non-IID IIoT Traffic via DDPG-Guided Adaptive Aggregation