federated learning

Digital Pathology and Artificial Intelligence in Veterinary Diagnostic Pathology: Implementation, Validation, and Emerging Applications

Digital pathology, driven by whole-slide imaging (WSI), digital slide management, and computational image analysis, is increasingly transforming veterinary diagnostic pathology. The integration of artificial intelligence (AI), including machine learning and deep learning, provides opportunities to automate or assist ta...

Censoring-Aware Rank Aggregation of Federated Top-k Explanation Prefixes: A Seed-Blocked Empirical Characterization against Weighted Borda Baselines

European Digital Campus: Lessons Learned from the Unite! implementation (Presentation at KTH, Stockholm, October 13, 2026)

How do we build a federated digital campus across Europe? Join us for an in-depth look at the practical realities of the Unite! alliance, where nine universities were developing their joint digital higher education. Building upon the latest Unite! -report and new developments, this unique event moves beyond theory to e...

RSIA: Defending Against Dynamic Collusive Attacks in Federated Learning

Federated learning, unlike centralized training, enables multiple clients to train a global model without exposing raw data, making it useful for applications such as medical diagnosis in smart healthcare and mobile edge networks. This setting is vulnerable to dynamic collusive attacks, where colluding clients coordina...

Federated learning with update level noise perturbation and blockchain audit logging for privacy-preserving healthcare ECG classification

Regenerative hospitality model using tourist municipalities using web scraping and unsupervised machine learning

Abstract Based on the research question: How can the regenerative hospitality actions undertaken by municipalities listed on the tourism map be classified? This study proposes a classification model for regenerative hospitality in Brazilian tourist municipalities. To achieve this aim, the study adopted a municipal-leve...

Blockchain-enabled privacy and trust in federated learning architectures

Artificial intelligence in central nervous system autoimmune diseases: from clinical decision support to mechanistic discovery and translation

Abstract Central nervous system (CNS) autoimmune diseases, including multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), autoimmune encephalitis (AE), and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), remain difficult to diagnose, stratify, and treat because clinical phen...

Multi-dimensional framework for context-aware IDS selection in edge-IIoT

The rapid expansion of Industrial Internet of Things networks has increased the demand for intrusion detection systems that balance accuracy, efficiency, and cost. Most studies have focused on accuracy alone, ignoring practical deployment trade-offs. We propose a multi-dimensional evaluation framework that integrates P...

Privacy in India’s digital credit ecosystem: a systematic literature review and policy directions

This study examines India’s digital credit ecosystem, exploring the relationship between technological efficiency and user rights, particularly data privacy. It aims to identify emerging trends, key concerns, and potential regulatory and technological interventions. The PRISMA framework guided a systematic review of sc...

Supplementary Material for "Characterizing the Maintaining Effect of Backdoors in Federated Learning"

Sequential Backdoor Retention Experiments This repository studies whether an implanted backdoor remains detectable after subsequent training. Each experiment compares continued benign training with training that injects a second backdoor. Evaluation measures retention of the first backdoor using both current and restor...

FedLCD: A Federated Learning Framework with Layer-Wise Consistency Detection

Federated learning enables multiple clients to collaboratively train a global model without sharing raw data, but it is vulnerable to Byzantine attacks, in which malicious clients upload manipulated model updates to disrupt training or degrade model accuracy. Existing Byzantine-robust aggregation methods typically oper...