splitDyn: Federated Split Neural Network for Distributed Edge AI Applications
Author:
Affiliation:
1. National Institute of Information and Communications Technology,Big Data Integration Research Center,Tokyo,Japan
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10020192/10020156/10020803.pdf?arnumber=10020803
Reference17 articles.
1. Efficiency and Fairness in the Resource Allocation to Device-to-Device Communications in LTE-A
2. SplitFed: When Federated Learning Meets Split Learning
3. The Hungarian method for the assignment problem
4. LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data;jiang;36th AAAI Conf Artif Intell,2022
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1. AOP: Towards Adaptive Offloading Point Approach in a Federated Learning Framework for Edge AI Applications;2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS);2023-12-17
2. Split and Federated Learning with Mobility in Vehicular Edge Computing;2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA);2023-05-23
3. A Transfer Learning Approach to Breast Cancer Classification in a Federated Learning Framework;IEEE Access;2023
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