research-article

FedDL: Federated Learning via Dynamic Layer Sharing for Human Activity Recognition

Authors:

Xiaomin Ouyang,

Guoliang XingAuthors Info & Claims

SenSys '21: Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems

November 2021

Pages 15 - 28

https://doi.org/10.1145/3485730.3485946

Published: 15 November 2021 Publication History

Abstract

Deep learning has been increasingly applied to improve human activity recognition (HAR) accuracy and reduce the human efforts of handcrafted feature extractions. Federated Learning (FL) is an emerging learning paradigm that enables the collaborative learning of a global model without exposing users' raw data. However, existing FL approaches yield unsatisfactory HAR performance as they fail to dynamically aggregate models according to the statistical diversity of users' data. In this paper, we propose FedDL, a novel federated learning system for HAR that can capture the underlying user relationships and apply them to learn personalized models for different users dynamically. Specifically, we design a dynamic layer sharing scheme that learns the similarity among users' model weights to form the sharing structure and merges models accordingly in an iterative, bottom-up layer-wise manner. FedDL merges local models based on the dynamic sharing scheme, significantly speeding up the convergence while maintaining high accuracy. We have implemented FedDL and evaluated using a new data set we collected using LiDAR and four public real-world datasets involving 178 users in total. The results show that FedDL outperforms several state-of-the-art FL paradigms in terms of model accuracy (by more than 15%), converging rate (by more than 70%), and communication overhead (about 30% reduction). Moreover, the testing results on the datasets of different scales show that FedDL has high scalability and hence can be deployed for large-scale real-world applications.

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Index Terms

FedDL: Federated Learning via Dynamic Layer Sharing for Human Activity Recognition
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Multi-task learning
        Transfer learning
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools

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SenSys '21: Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems

November 2021

686 pages

ISBN:9781450390972

DOI:10.1145/3485730

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Published: 15 November 2021

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SenSys '21: The 19th ACM Conference on Embedded Networked Sensor Systems

November 15 - 17, 2021

Coimbra, Portugal

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SenSys '21 Paper Acceptance Rate 25 of 139 submissions, 18%;

Overall Acceptance Rate 174 of 867 submissions, 20%

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Zhu RYang MWang Q(2024)ShuffleFL: Addressing Heterogeneity in Multi-Device Federated LearningProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596218:2(1-34)Online publication date: 15-May-2024
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Tabatabaie MHe S(2024)Driver Maneuver Interaction Identification with Anomaly-Aware Federated Learning on Heterogeneous Feature RepresentationsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314217:4(1-28)Online publication date: 12-Jan-2024
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Samikwa EDi Maio ABraun T(2024)DFL: Dynamic Federated Split Learning in Heterogeneous IoTIEEE Transactions on Machine Learning in Communications and Networking10.1109/TMLCN.2024.34092052(733-752)Online publication date: 2024
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