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TailorFL: Dual-Personalized Federated Learning under System and Data Heterogeneity

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Yaoxue ZhangAuthors Info & Claims

SenSys '22: Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems

Pages 592 - 606

https://doi.org/10.1145/3560905.3568503

Published: 24 January 2023 Publication History

Abstract

Federated learning (FL) enables distributed mobile devices to collaboratively learn a shared model without exposing their raw data. However, heterogeneous devices usually have limited and different available resources, i.e., system heterogeneity, for model training and communicating, while the diverse data distribution among devices, i.e., data heterogeneity, may result in significant performance degradation. In this paper, we propose TailorFL, a dual-personalized FL framework, which tailors a submodel for each device with personalized structure for training and personalized parameters for local inference. To achieve this, we first excavate the personalization principle for data heterogeneous FL via in-depth empirical studies, and based on which, we propose a resource-aware and data-directed pruning strategy that makes each device's submodel structure match its resource capability and correlate with its local data distribution. To aggregate the submodels while preserving their dual personalization properties, we design a scaling-based aggregation strategy that scales parameters with the pruning rate of submodels and aggregates the overlapped parameters. Moreover, to further promote beneficial and restrain detrimental collaborations among devices, we propose a server-assisted model-tuning mechanism, which dynamically tunes device's submodel structure at the server side with the global view of device's data distribution similarities. Extensive experiments demonstrate that compared to the status quo approaches, TailorFL achieves an average of 22% increase in inference accuracy, and reduces the memory, computation, and communication costs for model training simultaneously.

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Cited By

Zhou YDuan GQiu TZhang LTian LZheng XZhu Y(2024)Personalized Federated Learning Incorporating Adaptive Model Pruning at the EdgeElectronics10.3390/electronics1309173813:9(1738)Online publication date: 1-May-2024
https://doi.org/10.3390/electronics13091738
Shen LYang QCui KZheng YWei XLiu JHan JOkoshi TKo JLiKamWa R(2024)FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated ClientsProceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services10.1145/3643832.3661880(398-411)Online publication date: 3-Jun-2024
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Li XLiu SZhou ZGuo BXu YYu Z(2024)EchoPFLProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435608:1(1-22)Online publication date: 6-Mar-2024
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Index Terms

TailorFL: Dual-Personalized Federated Learning under System and Data Heterogeneity
1. Computing methodologies
  1. Machine learning
2. Human-centered computing
  1. Ubiquitous and mobile computing

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cover image ACM Conferences

SenSys '22: Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems

November 2022

1280 pages

ISBN:9781450398862

DOI:10.1145/3560905

General Chairs:
Jeremy Gummeson
University of Massachusetts Amherst
,
Sunghoon Ivan Lee
University of Massachusetts Amherst
,
Program Chairs:
Jie Gao
Rutgers University
,
Guoliang Xing
The Chinese University of Hong Kong

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This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 24 January 2023

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Young Elite Scientist Sponsorship Program by CAST
Guoqiang Institute, Tsinghua University
Natural Science Foundation of Hunan Province, China
National Key R&D Program of China
National Natural Science Foundation of China
Tsinghua University (AIR)-Asiainfo Technologies (China) Inc. Joint Research Center
Xiaomi AI Innovation Research
Key Research and Development Project of Hunan Province, China
Young Talents Plan of Hunan Province, China

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SenSys '22

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SenSys '22: The 20th ACM Conference on Embedded Networked Sensor Systems

November 6 - 9, 2022

Massachusetts, Boston

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SenSys '22 Paper Acceptance Rate 52 of 187 submissions, 28%;

Overall Acceptance Rate 174 of 867 submissions, 20%

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https://doi.org/10.3390/electronics13091738
Shen LYang QCui KZheng YWei XLiu JHan JOkoshi TKo JLiKamWa R(2024)FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated ClientsProceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services10.1145/3643832.3661880(398-411)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3643832.3661880
Li XLiu SZhou ZGuo BXu YYu Z(2024)EchoPFLProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435608:1(1-22)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643560
Mishra RGupta HBanga GDas S(2024)Fed-RAC: Resource-Aware Clustering for Tackling Heterogeneity of Participants in Federated LearningIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.337993335:7(1207-1220)Online publication date: Jul-2024
https://doi.org/10.1109/TPDS.2024.3379933
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Sabah FChen YYang ZAzam MAhmad NSarwar R(2024)Model optimization techniques in personalized federated learningExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122874243:COnline publication date: 25-Jun-2024
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Park JJoe-Wong C(2024)Federated Learning with Flexible ArchitecturesMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-031-70344-7_9(143-161)Online publication date: 22-Aug-2024
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