@inproceedings{2e6097b7b87c4bc2a32427508aaa435e,
title = "Harmonia: Accurate Federated Learning with All-Inclusive Dataset",
abstract = "Federated learning (FL) is an appealing model training technique that utilizes heterogeneous datasets and user devices, ensuring user data privacy. Existing FL research proposed device selection schemes to balance the computing speeds of devices. However, we observe that these schemes compromise prediction accuracy by 57. 7 \%. To solve this problem, we present Harmonia that enhances prediction accuracy, while also balancing the diverse computing speeds of devices. Our evaluation shows that Harmonia improves prediction accuracy by 1.7 x over existing schemes.",
keywords = "Client Selection, Collaborative Learning, Data Privacy, Distributed Machine Learning, Federated Learning",
author = "Wonmi Choi and Juyoung Ahn and Yeonho Yoo and Chuck Yoo and Gyeongsik Yang",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 17th IEEE International Conference on Cloud Computing, CLOUD 2024 ; Conference date: 07-07-2024 Through 13-07-2024",
year = "2024",
doi = "10.1109/CLOUD62652.2024.00041",
language = "English",
series = "IEEE International Conference on Cloud Computing, CLOUD",
publisher = "IEEE Computer Society",
pages = "302--304",
editor = "Chang, \{Rong N.\} and Chang, \{Carl K.\} and Jingwei Yang and Nimanthi Atukorala and Zhi Jin and Michael Sheng and Jing Fan and Kenneth Fletcher and Qiang He and Tevfik Kosar and Santonu Sarkar and Sreekrishnan Venkateswaran and Shangguang Wang and Xuanzhe Liu and Seetharami Seelam and Chandra Narayanaswami and Ziliang Zong",
booktitle = "Proceedings - 2024 IEEE 17th International Conference on Cloud Computing, CLOUD 2024",
address = "United States",
}