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openalexJournal of Intelligent Decision Making and Information Science2026-07-23Cited by 0

Secure Embedding Stabilization in Federated Learning

Aparna Sheetal Kamate

Secure Embedding Stabilization (SES) deals with embedding drift and instability in federated learning in non-IID and adversarial scenarios. This paper develops embedding variance minimization as a stability-constrained optimization problem and regularized secure alignment framework with privacy preserving aggregation. The suggested SES-FL model combines the variance-conscious regularization and encrypted centroid alignment to provide stabilized learning of representation among distributed clients. Experimental analysis shows better performance with accuracy of 89.74, precision 0.882, recall 0.876, and F1-score 0.879 and minimizes embedding variance to 0.072. The framework rounds off to 61 rounds, better than FedAvg, FedProx and FedDyn in terms of stability and communication efficiency (6.3 MB/round). These findings confirm that SES is a useful solution to strong, stable, and privacy-aware federated learning in heterogeneous data environments.

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