Harmonia: Accurate Federated Learning with All-Inclusive Dataset

Wonmi Choi; Juyoung Ahn; Yeonho Yoo; Chuck Yoo; Gyeongsik Yang

doi:10.1109/CLOUD62652.2024.00041

Harmonia: Accurate Federated Learning with All-Inclusive Dataset

Wonmi Choi, Juyoung Ahn, Yeonho Yoo, Chuck Yoo, Gyeongsik Yang

Department of Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Proceedings - 2024 IEEE 17th International Conference on Cloud Computing, CLOUD 2024
Editors	Rong N. Chang, Carl K. Chang, Jingwei Yang, Nimanthi Atukorala, Zhi Jin, Michael Sheng, Jing Fan, Kenneth Fletcher, Qiang He, Tevfik Kosar, Santonu Sarkar, Sreekrishnan Venkateswaran, Shangguang Wang, Xuanzhe Liu, Seetharami Seelam, Chandra Narayanaswami, Ziliang Zong
Publisher	IEEE Computer Society
Pages	302-304
Number of pages	3
ISBN (Electronic)	9798350368536
DOIs	https://doi.org/10.1109/CLOUD62652.2024.00041
Publication status	Published - 2024
Event	17th IEEE International Conference on Cloud Computing, CLOUD 2024 - Shenzhen, China Duration: 2024 Jul 7 → 2024 Jul 13

Publication series

Name	IEEE International Conference on Cloud Computing, CLOUD
ISSN (Print)	2159-6182
ISSN (Electronic)	2159-6190

Conference

Conference	17th IEEE International Conference on Cloud Computing, CLOUD 2024
Country/Territory	China
City	Shenzhen
Period	24/7/7 → 24/7/13

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

Client Selection
Collaborative Learning
Data Privacy
Distributed Machine Learning
Federated Learning

ASJC Scopus subject areas

Artificial Intelligence
Information Systems
Software

Access to Document

10.1109/CLOUD62652.2024.00041

Cite this

Choi, W., Ahn, J., Yoo, Y., Yoo, C., & Yang, G. (2024). Harmonia: Accurate Federated Learning with All-Inclusive Dataset. In R. N. Chang, C. K. Chang, J. Yang, N. Atukorala, Z. Jin, M. Sheng, J. Fan, K. Fletcher, Q. He, T. Kosar, S. Sarkar, S. Venkateswaran, S. Wang, X. Liu, S. Seelam, C. Narayanaswami, & Z. Zong (Eds.), Proceedings - 2024 IEEE 17th International Conference on Cloud Computing, CLOUD 2024 (pp. 302-304). (IEEE International Conference on Cloud Computing, CLOUD). IEEE Computer Society. https://doi.org/10.1109/CLOUD62652.2024.00041

Harmonia: Accurate Federated Learning with All-Inclusive Dataset. / Choi, Wonmi; Ahn, Juyoung; Yoo, Yeonho et al.
Proceedings - 2024 IEEE 17th International Conference on Cloud Computing, CLOUD 2024. ed. / Rong N. Chang; Carl K. Chang; Jingwei Yang; Nimanthi Atukorala; Zhi Jin; Michael Sheng; Jing Fan; Kenneth Fletcher; Qiang He; Tevfik Kosar; Santonu Sarkar; Sreekrishnan Venkateswaran; Shangguang Wang; Xuanzhe Liu; Seetharami Seelam; Chandra Narayanaswami; Ziliang Zong. IEEE Computer Society, 2024. p. 302-304 (IEEE International Conference on Cloud Computing, CLOUD).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Choi, W, Ahn, J, Yoo, Y, Yoo, C & Yang, G 2024, Harmonia: Accurate Federated Learning with All-Inclusive Dataset. in RN Chang, CK Chang, J Yang, N Atukorala, Z Jin, M Sheng, J Fan, K Fletcher, Q He, T Kosar, S Sarkar, S Venkateswaran, S Wang, X Liu, S Seelam, C Narayanaswami & Z Zong (eds), Proceedings - 2024 IEEE 17th International Conference on Cloud Computing, CLOUD 2024. IEEE International Conference on Cloud Computing, CLOUD, IEEE Computer Society, pp. 302-304, 17th IEEE International Conference on Cloud Computing, CLOUD 2024, Shenzhen, China, 24/7/7. https://doi.org/10.1109/CLOUD62652.2024.00041

Choi W, Ahn J, Yoo Y, Yoo C, Yang G. Harmonia: Accurate Federated Learning with All-Inclusive Dataset. In Chang RN, Chang CK, Yang J, Atukorala N, Jin Z, Sheng M, Fan J, Fletcher K, He Q, Kosar T, Sarkar S, Venkateswaran S, Wang S, Liu X, Seelam S, Narayanaswami C, Zong Z, editors, Proceedings - 2024 IEEE 17th International Conference on Cloud Computing, CLOUD 2024. IEEE Computer Society. 2024. p. 302-304. (IEEE International Conference on Cloud Computing, CLOUD). doi: 10.1109/CLOUD62652.2024.00041

Choi, Wonmi ; Ahn, Juyoung ; Yoo, Yeonho et al. / Harmonia : Accurate Federated Learning with All-Inclusive Dataset. Proceedings - 2024 IEEE 17th International Conference on Cloud Computing, CLOUD 2024. editor / Rong N. Chang ; Carl K. Chang ; Jingwei Yang ; Nimanthi Atukorala ; Zhi Jin ; Michael Sheng ; Jing Fan ; Kenneth Fletcher ; Qiang He ; Tevfik Kosar ; Santonu Sarkar ; Sreekrishnan Venkateswaran ; Shangguang Wang ; Xuanzhe Liu ; Seetharami Seelam ; Chandra Narayanaswami ; Ziliang Zong. IEEE Computer Society, 2024. pp. 302-304 (IEEE International Conference on Cloud Computing, CLOUD).

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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",

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Harmonia: Accurate Federated Learning with All-Inclusive Dataset

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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