@inproceedings{d5249f26590d4b37b066265ea6097583,
title = "Poster: Secure Federated Learning Network Based on Client Selection",
abstract = "Federated learning (FL) enables the training of a global model using clients' local datasets, leveraging their computing resources for efficient machine learning while preserving user privacy. This paper explores FL in wireless networks, focusing on client selection and bandwidth allocation as key factors impacting latency, covert constraint and energy consumption. We propose the per-round energy drift plus cost (PEDPC) algorithm to address this optimization problem from an online perspective. The performance of the PEDPC algorithm is validated through simulations, evaluating latency and energy consumption under both IID and non-IID data distributions.",
keywords = "client selection, computing resource, federated learning",
author = "Anguo Jiang and Huan Zhou and Rui Chen and Hengtao Wang and Shouzhi Xu",
note = "Publisher Copyright: {\textcopyright} 2024 Copyright held by the owner/author(s).; 22nd ACM Conference on Embedded Networked Sensor Systems, SenSys 2024 ; Conference date: 04-11-2024 Through 07-11-2024",
year = "2024",
month = nov,
day = "4",
doi = "10.1145/3666025.3699410",
language = "英语",
series = "SenSys 2024 - Proceedings of the 2024 ACM Conference on Embedded Networked Sensor Systems",
publisher = "Association for Computing Machinery, Inc",
pages = "865--866",
booktitle = "SenSys 2024 - Proceedings of the 2024 ACM Conference on Embedded Networked Sensor Systems",
}
