federated learning

Understanding Differential Privacy in Decentralized Federated Learning: A Controlled Privacy–Utility Comparison

Results show that clipping, perturbation, and the learning procedure jointly influence the observed privacy–utility behavior of centralized FL and sequential Decentralized Federated Learning.

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Transfer learning via fine-tuning pre-trained transformer models has gained significant suc- cess in delivering state-of-the-art results across various NLP tasks. In the absence of central- ized data, Federated Learning (FL) can benefit from distributed and private data of the FL edge clients for fine-tuning. However,...

Privacy-Preserving and Explainable Phishing Website Detection Using Federated Continual Learning

Phishing websites are continuously modified to imitate legitimate services, evade static blacklists, and exploit user trust. Machine-learning-based detection can identify previously unseen patterns, but conventional centralized training requires sensitive URL, browsing, webpage, and behavioral information to be collect...

Reproducible simulation code for energy-efficient federated multi-task learning

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Artificial Intelligence in Dementia with Lewy Bodies — Current Applications, Diagnostic Challenges, and Future Perspectives

Multiodal biomarker studies in mild cognitive impairment with Lewy bodies have suggested that combinations of MRI, EEG, and plasma markers may provide additional prognostic information beyond cognitive testing alone, but these findings should not be interpreted as evidence that AI is ready for autonomous clinical diagn...

Governing Data Flows and AI Innovation in a Fragmented Regulatory Environment

This chapter focuses on the intersectional nature of the essentials of data management privacy laws and strategic artificial intelligence (AI) implementation in intercountry setting. With data emerging as an important catalyst of innovation and competitive advantage organizations are beginning to struggle more to contr...

FedBCFA: a boundary-constrained feature alignment federated learning framework for multi-center breast MRI classification

Experimental results demonstrate that the proposed FedBCFA achieves favorable overall performance in multi-center breast MRI classification, effectively mitigates class confusion under non-IID conditions, and improves the discriminative stability for benign and malignant lesions.

A federated learning and recalibration pipeline for clinical prediction across heterogeneous regions: 30-day mortality after acute myocardial infarction

Privacy-Preserving and Fairness-Aware Secure Aggregation in Federated Learning

Edge-Driven Federated Learning System for Secure and Scalable Financial Data Sharing With Privacy Protection

The results indicate that the proposed edge-driven federated learning framework can support privacy-preserving and robust cross-institutional financial risk modeling and provides an effective solution for collaborative fraud detection and anti-money laundering under data isolation, heterogeneous edge environments, and...

An Explainable Decision Intelligence Architecture for Secure Data Sharing and Risk-Aware Resource Governance

The proposed framework combines hierarchical federated learning with multimodal data analysis, Self-Sovereign Identity, group signatures, differential privacy, SHAP based explanations, Siamese fine-tuning, and Pareto-based optimization.

Mechanical fault diagnosis under long-tailed data: an improved federated learning method integrating attention, asymmetric convolution, and margin calibration

Abstract Due to the inability to collect sufficient fault samples for specific types of gear and bearing faults, the data presents a long-tailed distribution, which hinders the effective construction of neural network diagnostic models. Furthermore, when Federated Learning (FL) methods are introduced to address this lo...