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DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset.

, , , , , and . CoRR, (2023)

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Federated Learning at the Edge: An Interplay of Mini-batch Size and Aggregation Frequency., , , , , and . INFOCOM Workshops, page 1-6. IEEE, (2023)An Online Control Approach of Collaborative Federated Learning with Constrained Resources., , , , , and . APNet, page 211-212. ACM, (2023)FlowShader: a Generalized Framework for GPU-accelerated VNF Flow Processing., , , , , , and . ICNP, page 1-12. IEEE, (2019)Cable: A framework for accelerating 5G UPF based on eBPF., , , , , , , , and . Comput. Networks, (February 2023)Antelope: A Framework for Dynamic Selection of Congestion Control Algorithms., , , , , , and . ICNP, page 1-11. IEEE, (2021)Task Placement and Resource Allocation for Edge Machine Learning: A GNN-based Multi-Agent Reinforcement Learning Paradigm., , , , , , and . CoRR, (2023)DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset., , , , , and . CoRR, (2023)TapFinger: Task Placement and Fine-Grained Resource Allocation for Edge Machine Learning., , , , and . INFOCOM, page 1-10. IEEE, (2023)A Machine Learning-Based Framework for Dynamic Selection of Congestion Control Algorithms., , , , , , , , and . IEEE/ACM Trans. Netw., 31 (4): 1566-1581 (August 2023)EdgeC3: Online Management for Edge-Cloud Collaborative Continuous Learning., , , , , and . SECON, page 411-419. IEEE, (2023)