MetaFusion-FL: A Cross Modality Federated Meta Learning Framework for Robust and Explainable Healthcare System
Bibliographic record
Abstract
Mpox is a re-emerging zoonotic viral disease that attracted the attention of the whole world because of its spreading transmission and clinical similarity with other skin diseases. It is highly important that this identification is fast and accurate, even in remotely located areas or resource-limited settings. However, the conventional centralized deep learning models exhibit severe limitations regarding data privacy, modality variation, and scalability across varied clinical environments. To this end, this paper presents MetaFusion-FL, a new federated meta-learning framework that combines cross-modality image analysis based on a hybrid Transformer-Capsule model with Hierarchical Attention-Based Multimodal Fusion (HAMFM). The model can work on multi-source images as input, namely smartphone images, dermoscopic images, and clinical images, which are processed locally at edge hospitals without raw data transmission. Reptile federated meta-learning strategy guarantees quick personalization of models and global generalization. When evaluated on a wide dataset, MetaFusion-FL has a higher classification accuracy of 99.46%, precision of 99.52%, recall of 99.40%, and F1-score of 99.46% compared to other current models, including ViT-RLXGBFL (99.12%) and ResViT-FLBoost (98.78%). The framework is also resistant to image noise and is consistent and stable across federated clients. Besides, SHAP and Grad-CAM++ explanations are used to ensure interpretability in a clinical context. MetaFusion-FL is therefore a leap in the development of AI-based, privacy-preserving, and generalizable skin disease classification, particularly Mpox.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".