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A Convergence Analysis of Distributed SGD with Communication-Efficient Gradient Sparsification., , , , and . IJCAI, page 3411-3417. ijcai.org, (2019)FedImpro: Measuring and Improving Client Update in Federated Learning., , , , , , and . CoRR, (2024)FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training., , , , , , , , , and . CoRR, (2023)VMRNN: Integrating Vision Mamba and LSTM for Efficient and Accurate Spatiotemporal Forecasting., , , , and . CoRR, (2024)Reliable and Efficient In-Memory Fault Tolerance of Large Language Model Pretraining., , , , , , , , , and . CoRR, (2023)Communication-Efficient Distributed Deep Learning: A Comprehensive Survey., , , , and . CoRR, (2020)Layer-wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence Guarantees., , , , and . CoRR, (2019)Communication-Efficient Distributed Deep Learning: Survey, Evaluation, and Challenges., , , , , and . CoRR, (2020)FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks., , , , , , , , , and 1 other author(s). CoRR, (2021)Layer-Wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence Guarantees., , , , and . ECAI, volume 325 of Frontiers in Artificial Intelligence and Applications, page 1467-1474. IOS Press, (2020)