The paper proposes a trustworthy DFML framework that mitigates task-distribution shift and corruption via synthetic task reconstruction, historical task replay through interpolation, and automatic filtering of untrustworthy models.
The ham10000 dataset, a large collection of multi-source dermato- scopic images of common pigmented skin lesions
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Task-Distributionally Robust Data-Free Meta-Learning
The paper proposes a trustworthy DFML framework that mitigates task-distribution shift and corruption via synthetic task reconstruction, historical task replay through interpolation, and automatic filtering of untrustworthy models.