Quantum-Evolutionary Neural Networks combine phase-shifted sine activations, evolutionary selection, and federated averaging, but the convergence and privacy proofs are asserted rather than derived, and the empirical evidence is too thin to support the stated claims.
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Quantum-Evolutionary Neural Networks for Multi-Agent Federated Learning
Quantum-Evolutionary Neural Networks combine phase-shifted sine activations, evolutionary selection, and federated averaging, but the convergence and privacy proofs are asserted rather than derived, and the empirical evidence is too thin to support the stated claims.