Towards cost-effective and resource-aware aggregation at Edge for Federated Learning

Ahmad Faraz Khan; Yuze Li; Xinran Wang; Sabaat Haroon; Haider Ali; Yue Cheng; Ali R. Butt; Ali Anwar

doi:10.1109/BigData59044.2023.10386691

Towards cost-effective and resource-aware aggregation at Edge for Federated Learning

Ahmad Faraz Khan, Yuze Li, Xinran Wang, Sabaat Haroon, Haider Ali, Yue Cheng, Ali R. Butt, Ali Anwar

Computer Science and Engineering

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

2 Scopus citations

Abstract

Federated Learning (FL) is a machine learning approach that addresses privacy and data transfer costs by computing data at the source. It's particularly popular for Edge and IoT applications where the aggregator server of FL is in resource-capped edge data centers for reducing communication costs. Existing cloud-based aggregator solutions are resource-inefficient and expensive at the Edge, leading to low scalability and high latency. To address these challenges, this study compares prior and new aggregation methodologies under the changing demands of IoT and Edge applications. This work is the first to propose an adaptive FL aggregator at the Edge, enabling users to manage the cost and efficiency trade-off. An extensive comparative analysis demonstrates that the design improves scalability by up to 4×, time efficiency by 8×, and reduces costs by more than 2× compared to extant cloud-based static methodologies.

Original language	English (US)
Title of host publication	Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023
Editors	Jingrui He, Themis Palpanas, Xiaohua Hu, Alfredo Cuzzocrea, Dejing Dou, Dominik Slezak, Wei Wang, Aleksandra Gruca, Jerry Chun-Wei Lin, Rakesh Agrawal
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	690-699
Number of pages	10
ISBN (Electronic)	9798350324457
DOIs	https://doi.org/10.1109/BigData59044.2023.10386691
State	Published - 2023
Event	2023 IEEE International Conference on Big Data, BigData 2023 - Sorrento, Italy Duration: Dec 15 2023 → Dec 18 2023

Publication series

Name	Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023

Conference

Conference	2023 IEEE International Conference on Big Data, BigData 2023
Country/Territory	Italy
City	Sorrento
Period	12/15/23 → 12/18/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

aggregation
edge computing
federated learning

Publisher link

10.1109/BigData59044.2023.10386691

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Khan, A. F., Li, Y., Wang, X., Haroon, S., Ali, H., Cheng, Y., Butt, A. R., & Anwar, A. (2023). Towards cost-effective and resource-aware aggregation at Edge for Federated Learning. In J. He, T. Palpanas, X. Hu, A. Cuzzocrea, D. Dou, D. Slezak, W. Wang, A. Gruca, J. C.-W. Lin, & R. Agrawal (Eds.), Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023 (pp. 690-699). (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData59044.2023.10386691

Towards cost-effective and resource-aware aggregation at Edge for Federated Learning. / Khan, Ahmad Faraz; Li, Yuze; Wang, Xinran et al.
Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. ed. / Jingrui He; Themis Palpanas; Xiaohua Hu; Alfredo Cuzzocrea; Dejing Dou; Dominik Slezak; Wei Wang; Aleksandra Gruca; Jerry Chun-Wei Lin; Rakesh Agrawal. Institute of Electrical and Electronics Engineers Inc., 2023. p. 690-699 (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023).

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

Khan, AF, Li, Y, Wang, X, Haroon, S, Ali, H, Cheng, Y, Butt, AR & Anwar, A 2023, Towards cost-effective and resource-aware aggregation at Edge for Federated Learning. in J He, T Palpanas, X Hu, A Cuzzocrea, D Dou, D Slezak, W Wang, A Gruca, JC-W Lin & R Agrawal (eds), Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023, Institute of Electrical and Electronics Engineers Inc., pp. 690-699, 2023 IEEE International Conference on Big Data, BigData 2023, Sorrento, Italy, 12/15/23. https://doi.org/10.1109/BigData59044.2023.10386691

Khan AF, Li Y, Wang X, Haroon S, Ali H, Cheng Y et al. Towards cost-effective and resource-aware aggregation at Edge for Federated Learning. In He J, Palpanas T, Hu X, Cuzzocrea A, Dou D, Slezak D, Wang W, Gruca A, Lin JCW, Agrawal R, editors, Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 690-699. (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023). doi: 10.1109/BigData59044.2023.10386691

Khan, Ahmad Faraz ; Li, Yuze ; Wang, Xinran et al. / Towards cost-effective and resource-aware aggregation at Edge for Federated Learning. Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. editor / Jingrui He ; Themis Palpanas ; Xiaohua Hu ; Alfredo Cuzzocrea ; Dejing Dou ; Dominik Slezak ; Wei Wang ; Aleksandra Gruca ; Jerry Chun-Wei Lin ; Rakesh Agrawal. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 690-699 (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023).

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AB - Federated Learning (FL) is a machine learning approach that addresses privacy and data transfer costs by computing data at the source. It's particularly popular for Edge and IoT applications where the aggregator server of FL is in resource-capped edge data centers for reducing communication costs. Existing cloud-based aggregator solutions are resource-inefficient and expensive at the Edge, leading to low scalability and high latency. To address these challenges, this study compares prior and new aggregation methodologies under the changing demands of IoT and Edge applications. This work is the first to propose an adaptive FL aggregator at the Edge, enabling users to manage the cost and efficiency trade-off. An extensive comparative analysis demonstrates that the design improves scalability by up to 4×, time efficiency by 8×, and reduces costs by more than 2× compared to extant cloud-based static methodologies.

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Towards cost-effective and resource-aware aggregation at Edge for Federated Learning

Abstract

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Keywords

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