Federated Prototype Learning for Cardiovascular Diseases Recognition Under Non-Independent and Non-Identically Distributed Heart Sounds
Abstract
Artificial intelligence (AI)-powered heart sound auscultation offers a noninvasive and accessible approach for diagnosing cardiovascular diseases (CVDs). However, training robust diagnostic models requires large-scale data, which is hindered by strict privacy regulations across isolated medical institutions. While federated learning (FL) addresses privacy concerns, real-world medical data exhibits severe non-independent and non-identically (Non-IID) characteristics, including label heterogeneity, intraclass representation diversity, and interclass imbalance. Existing FL methods primarily focus on personalized client models and struggle to construct a comprehensive global model capable of recognizing all disease subtypes under such extreme data heterogeneity. To overcome these challenges, we propose a novel two-stage decoupled federated prototype learning framework. In the first stage, we introduce a multicluster trainable prototype aggregation mechanism equipped with comprehensive local feature space calibration-incorporating $L_{2}$ normalization, learnable prototype radii, multilevel alignment, and adversarial domain adaptation-to dynamically handle label skew and intraclass diversity. In the second stage, we design a decoupled joint training paradigm that reconstructs a unified global classifier through privacy-preserving one-shot feature sharing and prior-based logit adjustment. Comprehensive experiments on the Yaseen valvular heart diseases (VHDs) and ZCHSound…
Read the paper
Related studies
- Active Learning during Federated Learning for Object Detection
- Fairness-aware federated learning
- Decentralized Federated Learning
- Building a Personally Identifiable Information Recognizer in a Privacy Preserved Manner Using Automated Annotation and Federated Learning
- Preservation of Patient Level Privacy: Federated Classification and Calibration Models
- Efficient explainable federated learning
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.