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cs.LG2026
Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors
Niraj Kumar, Harsh Kasyap
Post-hoc model explainers such as LIME, SHAP, and Integrated Gradients are widely deployed to audit models in high-stakes sensitive domains, including finance, healthcare, and soci…
cs.LG2026
CQSA: Byzantine-robust Clustered Quantum Secure Aggregation in Federated Learning
Arnab Nath, Harsh Kasyap
Federated Learning (FL) enables collaborative model training without sharing raw data. However, shared local model updates remain vulnerable to inference and poisoning attacks. Sec…
cs.LG2025
Fairness-Constrained Optimization Attack in Federated Learning
Harsh Kasyap, Minghong Fang, Zhuqing Liu +2
Federated learning (FL) is a privacy-preserving machine learning technique that facilitates collaboration among participants across demographics. FL enables model sharing, while re…