An Adaptive Gradient Clipping and Noise Injection Mechanism for Differentially Private Federated Learning
Differentially private federated learning must balance privacy protection against model accuracy and training efficiency. Static gradient clipping applies a fixed threshold throughout training and across model layers, which can cause excessive clipping when the threshold is too small or unnecessarily large noise when it is too large. This paper presents DDP-SA-adaptive, an adaptive gradient clipping and noise adding mechanism for differentially private federated learning with secure aggregation. At each communication round, every client determines a separate clipping threshold for each model layer from the median of its per-sample gradient norms. The resulting layer-wise thresholds adapt ...
| Authors | Wenjing Wei, Alla Jammine, Farid Nait-Abdesselam |
|---|---|
| Primary category | cs.CR |
| Published | 15 August 2026 |
| Last updated at source | 15 August 2026 |
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