Federated Continual Learning for Privacy-Preserving, Reliable and Interpretable Multi-Center Corneal Diseases Diagnosis

Abstract

Federated continual learning (FCL) is a distributed training framework that allows for learning from sequences of tasks on different centers under privacy-preserving. Although FCL has been extensively studied in fields such as image recognition and image segmentation, it remains unexplored in multi-center corneal diseases diagnosis, where data is inherently distributed and asynchronous while data privacy, reliability, and interpretability are urgently required. Therefore, this paper proposes Powderless for multi-center corneal diseases diagnosis, which can effectively transfer corneal diseases knowledge through prompt aggregation and prompt selection across various sequentially learned tasks from different centers under privacy. To further enhance diagnosis performance, ensure detection reliability, and improve interpretability, we design three key components: a multi-modal ensemble mechanism, an energy-based uncertainty estimation module, and a decision explanation module grounded in causal intervention. Comprehensive experimental results on the keratitis dataset demonstrate that our method achieves significant improvements compared to the base model, single-modality version, and local training in terms of both accuracy and the alignment between accuracy and uncertainty.

Keywords

federated continual learning / multi-center corneal diseases diagnosis / energy-based uncertainty estimation / interpretability

Author Contributions

H.P.: Conceptualization, investigation, writing, and revision. D.W.: Conceptualization, investigation, writing, and revision. All authors have read and agreed to the published version of the manuscript.

Funding

This paper is partially supported by Hong Kong Innovation and Technology Commission (ITC) grant #MHP/034/22.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest. Given the role as Editor-in-Chief, Dapeng Oliver Wu had no involvement in the peer review of this paper and had no access to information regarding its peer-review process. Full responsibility for the editorial process of this paper was delegated to another editor of the journal.

Use of AI and AI-Assisted Technologies

During the preparation of this work, the author(s) used nano-banana to provide figure references. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

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