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

Computer Science > Cryptography and Security [Submitted on 10 Jun 2026] Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices Kaan Arda Akyol, Jakub Kacper Szeląg, Aydin Abadi, Maha Alghamdi, Ghadah Albalawi, Ghouse Ibrahim Kaleelullah, Hilal Tutus, Sarah Al Subaiei, Shardul Kapse, Syed Mohammed Raheeb, Mujeeb Ahmed, Rehmat Ullah Continuous electrocardiography (ECG) monitoring could surface rhythm abnormalities before they escalate into cardiovascular events. However, a deployable system must satisfy three requirements simultaneously: legal-grade privacy (GDPR, HIPAA), real-time inference on constrained edge hardware, and detection quality under non-IID cross-hospital data. We design and evaluate an end-to-end federated system addressing all three for unsupervised 12-lead ECG anomaly detection on PTB-XL dataset, combining three autoencoder families (VanillaAE, ConvAE, VAE), Flower-based federated averaging (FedAvg) across ten simulated hospitals, client-side differentially private SGD (DP-SGD) with a Rényi-DP accountant, and 8-bit integer (INT8) post-training quantization with Raspberry Pi 4 benchmarking. Our main contributions are: an empirical characterization of how these mechanisms compose, practical DP-specific recommendations, and technical and security insights for a clinically sensitive setting. Federated learning matches or exceeds the centralized baseline across all architectures (ConvAE federated area under the ROC curve, AUROC, 0.782), and an \varepsilon sweep identifies \varepsilon=4 as the recommended clinical operating point. INT8 quantization roughly halves model size and cuts Pi 4 latency by up to 44% with <0.12% AUROC loss. Crucially, DP and quantization penalties are empirically independent, so practitioners need not trade a strong privacy guarantee for a compact edge footprint. To our knowledge, this is the first system combining federated learning, formal (\varepsilon,\delta)-DP, unsupervised reconstruction-based detection, and quantized AArch64 deployment. Comments: 9 pages, 4 figures, 6 tables. Preprint prepared in IEEE conference format. Submitted to: FLTA 2026 Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) MSC classes: 68T07, 68T09, 68P27, 62M10 ACM classes: I.2.6; I.5.4; J.3; C.2.4; C.3 Cite as: arXiv:2606.11556 [cs.CR]   (or arXiv:2606.11556v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.11556 Focus to learn more Submission history From: Jakub Szelag [view email] [v1] Wed, 10 Jun 2026 01:33:20 UTC (23 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs cs.AI cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)