arXiv AI

Hybrid Quantum-inspired Kolmogorov-Arnold Networks for Privacy-Aware Federated Biosignal Learning

arXiv:2608. 13914v1 Announce Type: cross Abstract: Electrocardiogram (ECG) recordings are sensitive biomedical data, limiting the ability of hospitals and wearable devices to share raw signals for centralized model training.

arXiv AI

Jun 11

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices

arXiv:2606. 11556v1 Announce Type: cross Abstract: Continuous electrocardiography (ECG) monitoring could surface rhythm abnormalities before they escalate into cardiovascular events.

By Kaan Arda Akyol, Jakub Kacper Szel\k{a}g, Aydin Abadi, Maha Alghamdi, Ghadah Albalawi, Ghouse Ibrahim Kaleelullah, Hilal Tutus, Sarah Al Subaiei, Shardul Kapse, Syed Mohammed Raheeb, Mujeeb Ahmed, Rehmat Ullah

efficiency benchmarks safety

arXiv Machine Learning

Jul 9

Dual Attention Heads for Personalized Federated Learning in ECG Classification

arXiv:2607. 06653v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training across institutions without sharing sensitive patient data.

By Kien Le, Joseph Lindley, Quoc Bao Phan, Tuy Tan Nguyen

llms benchmarks

arXiv Machine Learning

Jul 8

REAN: Reconstruction-aware ECG Anonymization Based on Privacy--Utility Orthogonality

arXiv:2607. 06037v1 Announce Type: cross Abstract: A shared electrocardiogram (ECG) is itself a biometric fingerprint that can re-identify a patient and reveal personal information.

By Taerin Ki, Sunghwan Park, Junyoung Park, Jaewoo Lee

safety

arXiv Machine Learning

Jul 27

A Robust Pipeline for Differentially Private Federated Learning on Imbalanced Clinical Data using SMOTETomek and FedProx

arXiv:2508. 10017v2 Announce Type: replace-cross Abstract: Federated Learning (FL) presents a groundbreaking approach for collaborative health research, allowing model training on decentralized data while safeguarding patient privacy.

By Rodrigo Tertulino

safety

arXiv AI

Jul 28

QFedPolyp: A Communication- and Inference-Efficient Federated Learning Framework for Polyp Segmentation

arXiv:2607. 22743v1 Announce Type: cross Abstract: Background and Objective: Automatic polyp segmentation supports computer-aided diagnosis and early colorectal cancer detec- tion.

By Madan Baduwal, Priyanka Paudel

computer-vision efficiency safety

arXiv Machine Learning

Jul 10

Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction

arXiv:2607. 08595v1 Announce Type: new Abstract: Cardiovascular disease risk prediction models often rely on data from a single institution or centrally pooled datasets.

By Hyunho Mo, Djura Smits, Mahlet A. Birhanu, Maarten J. G. Leening, Daniel Bos, Pim van der Harst, Esther E. Bron

safety

arXiv AI

Jun 10

Boosting ECG Classification Performance by Pre-training with Synthesized Data

arXiv:2606. 10802v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) typically require extensive datasets for effective training.

By Naoki Nonaka, Jun Seita

safety

arXiv AI

Jul 23

Recovering Clinical Utility Under Differential Privacy: Empirical Validation of Adaptive Federated Aggregation on Heterogeneous Cardiovascular Datasets

arXiv:2607. 19403v1 Announce Type: cross Abstract: Validating federated learning frameworks on real clinical data is an essential step between proof-of-concept demonstrations in controlled synthetic environments and deployment in real multicenter healthcare settings.

By Rodrigo Tertulino, Laercio Alencar, Ricardo Almeida

benchmarks safety

Hugging Face Trending Papers

Jul 9

Equivariant Quantum Clustering with Differential Privacy: Parameter-Efficient Privacy-Preserving Analysis Across Heterogeneous Sensitive Datasets

Privacy-preserving clustering is critical for analyzing sensitive data in healthcare, cybersecurity, and enterprise applications, where maintaining data confidentiality must be balanced with analytical performance. This paper presents Equivariant Quantum Clustering (EQC), a parameter-efficient framework that integrates symmetry-aware quantum circuits with differential privacy to improve the privacy-utility tradeoff.

benchmarks safety

arXiv Machine Learning

Jun 4

Federated Learning for Multi-Center Sepsis Early Prediction with Privacy-Preserving

arXiv:2606. 04338v1 Announce Type: new Abstract: Privacy-sensitive and distributed characteristics of multi-center medical data bring severe obstacles to centralized modeling for accurate early prediction of sepsis.

By Xixi Tian, Di Wu, Xiang Liu, Yiziting Zhu, Yujie Li, Xin Shu, Bin Yi

safety

arXiv AI

1d ago

Privacy-Preserving Decentralized Federated Learning via Explainable Adaptive Differential Privacy

arXiv:2509. 10691v3 Announce Type: replace-cross Abstract: Decentralized federated learning enables collaborative model training without a central server, but shared model updates can still leak sensitive information through inversion, reconstruction, and membership inference attacks.

By Fardin Jalil Piran, Zhiling Chen, Yang Zhang, Qianyu Zhou, Jiong Tang, Farhad Imani

llms efficiency benchmarks safety

arXiv AI

Jul 9

An Adaptive Differentially Private Federated Learning Framework

arXiv:2602. 06838v3 Announce Type: replace Abstract: Federated learning enables collaborative model training across distributed clients while preserving data privacy.

By Jin Wang, Hui Ma, Yajun Zhang, Xinjun Pei, Ming Yan, Fei Xing, Yikun Chen

safety

Related stories