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A Comprehensive Survey on Training Acceleration for Large Machine Learning Models in IoT., , , , , , , и . IEEE Internet Things J., 9 (2): 939-963 (2022)RuleDRL: Reliability-Aware SFC Provisioning With Bounded Approximations in Dynamic Environments., , , , , , и . IEEE Trans. Serv. Comput., 16 (5): 3651-3664 (сентября 2023)A bidirectional DNN partition mechanism for efficient pipeline parallel training in cloud., , , , и . J. Cloud Comput., 12 (1): 22 (декабря 2023)A Learning-Based Incentive Mechanism for Federated Learning., , , , и . IEEE Internet Things J., 7 (7): 6360-6368 (2020)BonsaiKV: Towards Fast, Scalable, and Persistent Key-Value Stores with Tiered, Heterogeneous Memory System., , , , и . Proc. VLDB Endow., 17 (4): 726-739 (декабря 2023)An efficient split learning framework for recurrent neural network in mobile edge environment., , , , и . RACS, стр. 131-138. ACM, (2022)Communication-efficient Federated Learning via Quantized Clipped SGD., , и . WASA (1), том 12937 из Lecture Notes in Computer Science, стр. 559-571. Springer, (2021)Joint Service Placement and Computation Offloading in Mobile Edge Computing: An Auction-based Approach., , , и . ICPADS, стр. 256-265. IEEE, (2020)From Deterioration to Acceleration: A Calibration Approach to Rehabilitating Step Asynchronism in Federated Optimization., , , , , , и . CoRR, (2021)Heterogeneity-Aware Gradient Coding for Tolerating and Leveraging Stragglers., , , , , , и . IEEE Trans. Computers, 71 (4): 779-794 (2022)