Separating Expert Retention from Autonomous Source Inference in Raw-ECG-Replay-Free Continual ECG Deployment
arXiv:2607.01674v1 Announce Type: new Abstract: In multi-source ECG deployment, models may need to incorporate new data sources when earlier raw ECGs cannot be retained or replayed. Freezing a pretrained backbone and assigning each source an isolated classifier prevents parameter interference, but deployment still requires selecting an expert when source metadata are unavailable. We study this distinction through \ours{}, an incremental expert bank built on frozen 1024-dimensional ECGFounder features. Each arriving domain adds a balanced-softmax linear expert, while a lightweight router is fit
This development addresses a critical challenge in real-world AI deployment where data privacy regulations or logistical constraints prevent continuous access to raw data, necessitating new methods for model adaptation.
It introduces a novel approach for continual learning in sensitive domains like healthcare (ECG), allowing AI systems to adapt to new data sources and environments without compromising privacy or incurring prohibitive storage costs.
This research advances the methodology for maintaining AI model performance in dynamic, data-constrained environments by separating expert retention from autonomous source inference, enabling more robust and privacy-preserving deployments.
- · Healthcare AI providers
- · Privacy-focused AI developers
- · Continual learning research community
- · AI models requiring constant raw data access
- · Data-intensive legacy continual learning methods
AI models in domains with strict data retention policies can be more easily updated and deployed.
This could lead to a proliferation of AI applications in privacy-sensitive sectors due to improved compliance and adaptability.
The reduced need for raw data retention might lower the operational costs for AI systems, making them more accessible to a wider range of organizations.
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Read at arXiv cs.AI