Papers by Md Abdullah Al Hafiz Khan
FedPAGR: Federated Prototype Alignment via Geometric Refinement for Heterogeneous Architectures (2026.acl-srw)
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| Challenge: | Federated learning with heterogeneous client architectures can be difficult due to semantic drift and poor inter-class separation. |
| Approach: | They propose a framework where heterogeneous clients exchange class prototypes with a central server and refine them through a geometric regularization objective. |
| Outcome: | The proposed framework achieves highest ensemble accuracy across all four image datasets and highest local test accuracy on low-class and clinical tasks. |