AI RESEARCH
When Differential Privacy Meets Wireless Federated Learning: An Improved Analysis for Privacy and Convergence
arXiv CS.LG
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ArXi:2603.19040v1 Announce Type: new Differentially private wireless federated learning (DPWFL) is a promising framework for protecting sensitive user data. However, foundational questions on how to precisely characterize privacy loss remain open, and existing work is further limited by convergence analyses that rely on restrictive convexity assumptions or ignore the effect of gradient clipping. To overcome these issues, we present a comprehensive analysis of privacy and convergence for DPWFL with general smooth non-convex loss objectives.