Waste Not, Want Not: Service Migration-Assisted Federated Intelligence for Multi-Modality Mobile Edge Computing

Author:

Zhou Hansong1ORCID,Wang Shaoying2ORCID,Jiang Chutian2ORCID,Zhang Xiaonan1ORCID,Guo Linke3ORCID,Yuan Yukun4ORCID

Affiliation:

1. Florida State University, Tallahassee, Florida, United States

2. Florida State University, Tallahassee, Florida, USA

3. Clemson University, Clemson, South Carolina, United States

4. University of Tennessee at Chattanooga, Chattanooga, Tennessee, United States

Funder

NSF (National Science Foundation)

Publisher

ACM

Reference34 articles.

1. Edge Computing for Smart Health: Context-Aware Approaches, Opportunities, and Challenges

2. Communicating While Computing: Distributed mobile cloud computing over 5G heterogeneous networks

3. Ilker Bozcan and Erdal Kayacan . 2020 . AU-AIR: A Multi-modal Unmanned Aerial Vehicle Dataset for Low Altitude Traffic Surveillance. In 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, Virtual, 8504--8510 . Ilker Bozcan and Erdal Kayacan. 2020. AU-AIR: A Multi-modal Unmanned Aerial Vehicle Dataset for Low Altitude Traffic Surveillance. In 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, Virtual, 8504--8510.

4. nuScenes: A Multimodal Dataset for Autonomous Driving

5. Timothy Castiglia Shiqiang Wang and Stacy Patterson. 2022. Flexible Vertical Federated Learning with Heterogeneous Parties. https://arxiv.org/abs/2208.12672 Timothy Castiglia Shiqiang Wang and Stacy Patterson. 2022. Flexible Vertical Federated Learning with Heterogeneous Parties. https://arxiv.org/abs/2208.12672

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