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Why Is Public Pretraining Necessary for Private Model Training?

, , , , , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 10611-10627. PMLR, (2023)

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Creation of ESL power models for communication architectures using automatic calibration., , , , , , and . DAC, page 58:1-58:58. ACM, (2013)Adaptive Asynchronous Federated Learning in Resource-Constrained Edge Computing., , , , , , and . IEEE Trans. Mob. Comput., 22 (2): 674-690 (2023)MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation., , , , , and . CoRR, (2023)Why Is Public Pretraining Necessary for Private Model Training?, , , , , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 10611-10627. PMLR, (2023)Byzantine-Robust Federated Learning with Optimal Statistical Rates., , , , , , and . AISTATS, volume 206 of Proceedings of Machine Learning Research, page 3151-3178. PMLR, (2023)TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan Backdoors in AI Systems., , , , and . CoRR, (2019)Ferrari: A Personalized Federated Learning Framework for Heterogeneous Edge Clients., , , , , and . IEEE Trans. Mob. Comput., 23 (10): 10031-10045 (October 2024)BOSE: Block-Wise Federated Learning in Heterogeneous Edge Computing., , , , , , and . IEEE/ACM Trans. Netw., 32 (2): 1362-1377 (April 2024)Asynchronous Decentralized Federated Learning for Heterogeneous Devices., , , , , and . IEEE/ACM Trans. Netw., 32 (5): 4535-4550 (October 2024)Data Capsule: A New Paradigm for Automatic Compliance with Data Privacy Regulations., , , , , , and . Poly/DMAH@VLDB, volume 11721 of Lecture Notes in Computer Science, page 3-23. Springer, (2019)