ArticleScientific reports2025
Efficient lightweight privacy data anomaly detection solution with robust aggregation.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Detecting anomalies in privacy-sensitive text under federated learning requires robustness against model poisoning and computational efficiency. This study presents an integrated framework combining an early-exit RoBERTa classifier ([Formula: see text]-RoBERTa) with a robust federated layered aggregation strategy (RFLA). [Formula: see text]-RoBERTa incorporates multi-stage exits and a spatio-temporal convolutional-LSTM fusion module to reduce inference cost while maintaining high detection accuracy. RFLA enhances server-side resilience by filtering and reweighting client updates through dimensionality reduction, density clustering, fallback adjustment, and Mahalanobis-based weighting. Experiments on four public datasets demonstrate consistent performance gains: E
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