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Priyal Parmar

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#federated learning Open access Sep 2026

Noise Placement, Privacy Accounting, and Structured Clipping in Client-Level Differentially Private Federated Learning An Empirical Study

This paper presents a controlled empirical study of three implementation choices in client-level differentially private federated learning: noise placement, privacy accounting, and structured clipping. Experiments are conducted under a trusted-server threat model with client-level add/remove adjacency across CIFAR-10,...

Priyal Parmar · 0 citations
#federated learning Open access Sep 2026

Noise Placement, Privacy Accounting, and Structured Clipping in Client-Level Differentially Private Federated Learning An Empirical Study

This paper presents a controlled empirical study of three implementation choices in client-level differentially private federated learning: noise placement, privacy accounting, and structured clipping. Experiments are conducted under a trusted-server threat model with client-level add/remove adjacency across CIFAR-10,...

Priyal Parmar · 0 citations

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