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Author

Ehsan Hallaji

University of Windsor

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#artificial intelligence Open access Feb 2025

FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in Federated Learning

FedNIA injects random noise inputs to analyze the layerwise activation patterns in client models leveraging an autoencoder that detects abnormal behaviors indicative of data poisoning and can defend against diverse attack types, including sample poisoning, label flipping, and backdoors.

Ehsan Hallaji, R. Razavi-Far, Mehrdad Saif · 2 citations
Open access Aug 2026

Rethinking Continual Learning Through Self-Adaptive Learning

This perspective explores how learning systems may progress beyond reactive adaptation toward autonomous recognition, policy selection, and context-sensitive regulation of learning behavior under persistent uncertainty and positions SAL as a conceptual systems framework for organizing future research on resilient, long...

Ehsan Hallaji, R. Razavi-Far · 0 citations

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