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. A prior architectural study by the same authors (Tertulino and Alencar, 2026) demonstrated, on a synthetic six-feature benchmark, that server-side adaptive optimization acts as a temporal denoiser for Differential Privacy noise, answ
Record details
Published: 23 July 2026
Source: arXiv cs.CY
Category: Research
Topics: Privacy · Healthcare · Environment
Retrieved: 23 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
RAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge Deployment
arXiv cs.LG · 29 July 2026
Recent advances in AI-based mobile robots for human companionship: survey
Artificial Intelligence Review · 29 June 2026
Toward Zero-Egress Psychiatric AI: On-Device LLM Deployment for Privacy-Preserving Mental Health Decision Support
arXiv · 20 April 2026
Air quality index AQI classification based on hybrid particle swarm and grey wolf optimization with ensemble machine learning model
OpenAlex · 5 January 2026
Clinical Pathways as Safety Specifications for Physical AI in Hospital Wards
arXiv cs.CY · 23 July 2026
Hybrid fuzzy C-means and deep learning framework for intelligent fault classification in solar PV systems
Frontiers in Artificial Intelligence · 23 July 2026
How to cite this record
ethics.ai (23 July 2026), “Recovering Clinical Utility Under Differential Privacy: Empirical Validation of Adaptive Federated Aggregation on Heterogeneous Cardiovascular Datasets,” evidence record 12641, https://ethics.ai/record/12641 (originally published by arXiv cs.CY).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.