Privacy loss in DPWFL converges to a constant rather than diverging, with convergence guarantees for non-convex losses including gradient clipping and an explicit privacy-utility trade-off.
With the growing prevalence of wireless networks, FL has been increasingly deployed over wireless channels, giving rise to wireless federated learning (WFL)
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When Differential Privacy Meets Wireless Federated Learning: An Improved Analysis for Privacy and Convergence
Privacy loss in DPWFL converges to a constant rather than diverging, with convergence guarantees for non-convex losses including gradient clipping and an explicit privacy-utility trade-off.