Author

Yuisa Kaliok

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#federated learning Open access Sep 2026

Privacy-Preserving Federated Learning Across Heterogeneous Distributed Datasets: Quantum-Inspired Mechanisms, Explainable Classifiers and Multi-Domain Empirical Evidence

This paper addresses vulnerabilities in standard federated learning aggregation against gradient inversion attacks by evaluating three layered privacy-enhancing mechanisms: classical differential privacy (DP) noise injection, homomorphic encryption, and a novel quantum-inspired random unitary rotation of embedding vect...

Alepera Rasaed, Yuisa Kaliok · Figshare · 0 citations

#federated learning Open access Sep 2026

Privacy-Preserving Federated Learning Across Heterogeneous Distributed Datasets: Quantum-Inspired Mechanisms, Explainable Classifiers and Multi-Domain Empirical Evidence

Alepera Rasaed, Yuisa Kaliok · Figshare · 0 citations