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This project is focused on the design, development, and deployment of federated solutions to problems in the framework of mobile edge computing.

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khamfroush-lab/Fed-MEC

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Fed-MEC

This project is focused on the design, development, and deployment of federated solutions to problems in the framework of mobile edge computing.


In this repository, you can find source code related to the following papers:

  • Hosseinzadeh, M. Wachal, A., Khamfroush, H., Lucani, D. E., "Optimal Accuracy-Time Trade-off For Deep Learning Services in Edge Computing Systems," IEEE ICCC, 2021.
    • Code can be found in Distributed-Client-Selection-FL.
    • To evaluate the proposed approach in this paper, we built a testbed in Marksburry building at Computer Science department at University of kentucky consisting of 2 Raspberry Pi4, 3 Raspberry Pi3, a NetGear router, and a Linux Desktop. The picture of the testbed is here: Screenshot
  • Hudson, N., Khamfroush, H., Lucani, D. E., "QoS-Aware Placement of Deep Learning Services on the Edge with Multiple Service Implementations," IEEE ICCCN Workshops, 2021.
    • Code can be found in PIES-Service-Placement-Code.
    • Draft of the full manuscript can be found in PIES_Service_Placement.

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This project is focused on the design, development, and deployment of federated solutions to problems in the framework of mobile edge computing.

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