Advances and open problems in federated learning

von Kairouz, Peter
Veröffentlicht 2021

THE DISTRIBUTED DISCRETE GAUSSIAN MECHANISM FOR FEDERATED LEARNING WITH SECURE AGGREGATION

von KAIROUZ, PETER
Veröffentlicht in International Conference on Machine Learning (38. : 2021 : Online) International Conference on Machine Learning (ICML 2021 ; Part 7 of 16 (2022)

The Skellam Mechanism for Differentially Private Federated Learning

von Agarwal, Naman
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 7 of 36 (2022)

BREAKING THE COMMUNICATION-PRIVACY-ACCURACY TRILEMMA

von Chen, Wei-Ning
Veröffentlicht in NeurIPS (34. : 2020 : Online) 34th Conference on Neural Information Processing Systems (NeurIPS 2020) ; Volume 5 of 27 (2021)

Pointwise Bounds for Distribution Estimation under Communication Constraints

von Chen, Wei-Ning
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 29 of 36 (2022)

Privacy Amplification via Random Check-Ins

von Balle, Borja
Veröffentlicht in NeurIPS (34. : 2020 : Online) 34th Conference on Neural Information Processing Systems (NeurIPS 2020) ; Volume 6 of 27 (2021)

CONTEXT-AWARE LOCAL DIFFERENTIAL PRIVAQCY

von Acharya, Jayadev
Veröffentlicht in International Conference on Machine Learning (37. : 2020 : Online) 37th International Conference on Machine Learning (ICML 2020) ; Part 1 of 15 (2021)

The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning

von Chen, Wei-Ning
Veröffentlicht in International Conference on Machine Learning (39. : 2022 : Baltimore, Md.; Online) International Conference on Machine Learning (ICML 2022) ; Part 4 of 33 (2023)

PRACTICAL AND PRIVATE (DEEP) LEARNING WITHOUT SAMPLING OR SHUFFLING

von KAIROUZ, PETER
Veröffentlicht in International Conference on Machine Learning (38. : 2021 : Online) International Conference on Machine Learning (ICML 2021 ; Part 7 of 16 (2022)