Research map / Federated learning and privacy / Differential privacy

DP-FedAdamW: An Efficient Optimizer for Differentially Private Federated Large Models

CVPR 2026 · Federated learning and privacy › Differential privacy · approach: Differentially private federated learning · 0 citations (OpenAlex)

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Closest papers by meaning

• Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning (ICML 2024 · similarity 0.91)
• Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers, and Gradient Clipping (NeurIPS 2025 · similarity 0.90)
• Towards the Robustness of Differentially Private Federated Learning (AAAI 2024 · similarity 0.90)
• One-shot Empirical Privacy Estimation for Federated Learning (ICLR 2024 · similarity 0.89)
• Mitigating the Privacy–Utility Trade-off in Decentralized Federated Learning via f-Differential Privacy (NeurIPS 2025 · similarity 0.89)
• DictPFL: Efficient and Private Federated Learning on Encrypted Gradients (NeurIPS 2025 · similarity 0.88)