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Federated Learning With Client Selection and Gradient Compression in Heterogeneous Edge Systems.

, , , , , and . IEEE Trans. Mob. Comput., 23 (5): 5446-5461 (May 2024)

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Decentralized Federated Learning With Intermediate Results in Mobile Edge Computing., , , , and . IEEE Trans. Mob. Comput., 23 (1): 341-358 (January 2024)Federated Learning With Client Selection and Gradient Compression in Heterogeneous Edge Systems., , , , , and . IEEE Trans. Mob. Comput., 23 (5): 5446-5461 (May 2024)CoopFL: Accelerating federated learning with DNN partitioning and offloading in heterogeneous edge computing., , , , and . Comput. Networks, (2023)FAST: Enhancing Federated Learning Through Adaptive Data Sampling and Local Training., , , , , and . IEEE Trans. Parallel Distributed Syst., 35 (2): 221-236 (February 2024)Heterogeneity-Aware Federated Learning with Adaptive Client Selection and Gradient Compression., , , , and . INFOCOM, page 1-10. IEEE, (2023)FedMP: Federated Learning through Adaptive Model Pruning in Heterogeneous Edge Computing., , , , , and . ICDE, page 767-779. IEEE, (2022)FedSA: A Semi-Asynchronous Federated Learning Mechanism in Heterogeneous Edge Computing., , , , , and . IEEE J. Sel. Areas Commun., 39 (12): 3654-3672 (2021)Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning., , , , and . CoRR, (2022)Computation and Communication Efficient Federated Learning With Adaptive Model Pruning., , , , , , and . IEEE Trans. Mob. Comput., 23 (3): 2003-2021 (March 2024)Joint Model Pruning and Topology Construction for Accelerating Decentralized Machine Learning., , , , , and . IEEE Trans. Parallel Distributed Syst., 34 (10): 2827-2842 (October 2023)