Joint Resource Allocation for Efficient Federated Learning in Internet of Things Supported by Edge Computing
Author:
Funder
China Mobile Research Institute
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9473476/9473480/09473734.pdf?arnumber=9473734
Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks;2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC);2023-09-05
2. Low-Latency Federated Learning With DNN Partition in Distributed Industrial IoT Networks;IEEE Journal on Selected Areas in Communications;2023-03
3. Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G;IEEE Access;2023
4. Optimizing Execution Time and Costs of Cross-Silo Federated Learning Applications with Datasets on different Cloud Providers;2022 IEEE 34th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD);2022-11
5. Towards a Federated Learning Framework on a Multi-Cloud Environment;2022 International Symposium on Computer Architecture and High Performance Computing Workshops (SBAC-PADW);2022-11
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