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Federated Conformal Predictors for Distributed Uncertainty Quantification., , , , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 22942-22964. PMLR, (2023)Accelerating Gradient Boosting Machine., , , и . CoRR, (2019)Defection-Free Collaboration between Competitors in a Learning System., , и . CoRR, (2024)Collaborative Heterogeneous Causal Inference Beyond Meta-analysis., , и . ICML, OpenReview.net, (2024)Practical Low-Rank Communication Compression in Decentralized Deep Learning., , и . NeurIPS, (2020)Breaking the centralized barrier for cross-device federated learning., , , , , , и . NeurIPS, стр. 28663-28676. (2021)RelaySum for Decentralized Deep Learning on Heterogeneous Data., , , , , , и . NeurIPS, стр. 28004-28015. (2021)Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data., , , и . ICML, том 139 из Proceedings of Machine Learning Research, стр. 6654-6665. PMLR, (2021)Privacy Can Arise Endogenously in an Economic System with Learning Agents., , , , и . FORC, том 295 из LIPIcs, стр. 9:1-9:22. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2024)PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization., , и . NeurIPS, стр. 14236-14245. (2019)