arXiv:2607.06653v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training across institutions without sharing sensitive patient data. However, the inherent heterogeneity of electrocardiogram (ECG) data across healthcare providers presents significant technical challenges for robust classification. We propose FedDualAtt, a personalized federated learning approach that splits transformer attention heads into global and local branches. Global heads are aggregated via FedAvg to capture shared cross-site patterns, while local heads remain client-specific to adapt
The proliferation of federated learning in sensitive domains like healthcare is driving innovation to overcome data heterogeneity challenges while maintaining privacy.
This research addresses a critical privacy-preserving AI method, making it more robust and effective for real-world applications, especially in distributed healthcare systems.
Federated learning models can now more effectively handle diverse datasets from various institutions without sacrificing local data specificity, improving overall model performance and reliability.
- · Healthcare Providers
- · Personalized Medicine Developers
- · AI/ML Research Institutions
- · Data Privacy Solutions
- · Traditional Centralized Data Models
- · Generic AI/ML Approaches in Healthcare
Improved accuracy and utility of federated learning models in healthcare, particularly for ECG classification.
Accelerated adoption of federated learning in other sensitive data domains beyond healthcare due to enhanced robustness.
Potential for new regulatory frameworks and industry standards that leverage personalized federated learning for privacy-preserving AI development.
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Read at arXiv cs.LG