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Privacy-preserving and bandwidth-efficient federated learning: an application to in-hospital mortality prediction., , , и . CHIL, стр. 25-35. ACM, (2021)Private Set Generation with Discriminative Information., , и . NeurIPS, (2022)Differentially Private Federated Learning for Bandwidth and Energy Constrained Environments. (Apprentissage fédéré avec confidentialité différentielle pour les environnements contraints en bande passante et énergie).. Grenoble Alpes University, France, (2021)Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data., , , и . CHIL, том 174 из Proceedings of Machine Learning Research, стр. 411-425. PMLR, (2022)Compression Boosts Differentially Private Federated Learning., , , и . EuroS&P, стр. 304-318. IEEE, (2021)Node-based optimization of LoRa transmissions with Multi-Armed Bandit algorithms., , , , и . ICT, стр. 521-526. IEEE, (2018)Compression Boosts Differentially Private Federated Learning., , , и . CoRR, (2020)Client-specific Property Inference against Secure Aggregation in Federated Learning., , и . WPES@CCS, стр. 45-60. ACM, (2023)Constrained differentially private federated learning for low-bandwidth devices., , , и . UAI, том 161 из Proceedings of Machine Learning Research, стр. 1756-1765. AUAI Press, (2021)FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations., , , и . Proc. Priv. Enhancing Technol., 2024 (3): 372-390 (2024)