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SEAD Counter: Self-Adaptive Counters With Different Counting Ranges.

, , , , , , , , and . IEEE/ACM Trans. Netw., 30 (1): 90-106 (2022)

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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)BOSE: Block-Wise Federated Learning in Heterogeneous Edge Computing., , , , , , and . IEEE/ACM Trans. Netw., 32 (2): 1362-1377 (April 2024)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)Ferrari: A Personalized Federated Learning Framework for Heterogeneous Edge Clients., , , , , and . IEEE Trans. Mob. Comput., 23 (10): 10031-10045 (October 2024)TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan Backdoors in AI Systems., , , , and . CoRR, (2019)Asynchronous Decentralized Federated Learning for Heterogeneous Devices., , , , , and . IEEE/ACM Trans. Netw., 32 (5): 4535-4550 (October 2024)Accelerating Decentralized Federated Learning in Heterogeneous Edge Computing., , , , and . IEEE Trans. Mob. Comput., 22 (9): 5001-5016 (September 2023)