Research map / Federated learning and privacy / Differential privacy
Accurate, Private, Secure, Federated U-statistics with Higher Degree
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Closest papers by meaning
- Mitigating the Privacy–Utility Trade-off in Decentralized Federated Learning via f-Differential Privacy (NeurIPS 2025 · similarity 0.86)
- One-shot Empirical Privacy Estimation for Federated Learning (ICLR 2024 · similarity 0.86)
- Towards Trustworthy Federated Learning with Untrusted Participants (ICML 2025 · similarity 0.85)
- Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off (ICML 2025 · similarity 0.85)
- On Differentially Private U Statistics (NeurIPS 2024 · similarity 0.85)
- Sketched Gaussian Mechanism for Private Federated Learning (NeurIPS 2025 · similarity 0.85)
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