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FedAli-PHAR: personalized federated learning with optimal transport-based prototype alignment for robust human activity recognition in pervasive computing

R. Satheeskumar S. Oyyathevan J. Thimmiaraja M. Suresh Talatoti Ratna Kumar
Jul 2026 · CCF Transactions on Pervasive Computing and Interaction · Vol 8, pp. 451 - 465 · 0 citations · 41 references

TL;DR

FedAli-PHAR is proposed, a novel personalized federated learning framework that extends the Alignment with Prototypes (ALP) layer using optimal transport (Sinkhorn-Knopp algorithm) to align embeddings with learnable local and global prototypes.

DOI Semantic Scholar
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