Meta-HAR: Federated Representation Learning for Human Activity Recognition
Open accessAbstract
Human activity recognition (HAR) based on mobile sensors plays an important role in ubiquitous computing. However, the rise of data regulatory constraints precludes collecting private and labeled signal data from personal devices at scale. Thanks to the growth of computational power on mobile devices, federated learning has emerged as a decentralized alternative solution to model training, which iteratively aggregates locally updated models into a shared global model, therefore being able to leverage decentralized, private data without central collection. However, the effectiveness of federated learning for HAR is affected by the fact that each user has different activity types and even a different signal distribution for the same activity type. Furthermore, it is uncertain if a single global model trained can generalize well to individual users or new users with heterogeneous data. In this paper, we propose Meta-HAR, a federated representation learning framework, in which a signal embedding network is meta-learned in a federated manner, while the learned signal representations are further fed into a personalized classification network at each user for activity prediction. In order to boost the representation ability of the embedding network, we treat the HAR problem at each user as a different task and train the shared embedding network through a Model-Agnostic Meta-learning framework, such that the embedding network can generalize to any individual user. Personalization is further achieved on top of the robustly learned representations in an adaptation procedure. We conducted extensive experiments based on two publicly available HAR datasets as well as a newly created HAR dataset. Results verify that Meta-HAR is effective at maintaining high test accuracies for individual users, including new users, and significantly outperforms several baselines, including Federated Averaging, Reptile and even centralized learning in certain cases. Our collected dataset will be open-sourced to facilitate future development in the field of sensor-based human activity recognition.
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Federated learning is a trending approach to resolve privacy issues in learning problems.
“Federated learning is a trending approach to resolve privacy issues in learning problems [275,276,277,278].”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [278]
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215 · see citing works
Practical Secure Aggregation for Privacy-Preserving Machine Learning
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3133982 · Cited by 3,956 · see citing works
Towards Federated Learning at Scale: System Design
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečný, J., Mazzocchi, S., McMahan, H. B., et al. (2019) · arXiv · doi:10.48550/arxiv.1902.01046 · Cited by 2,334 · see citing works
Federated Learning with Non-IID Data
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018) · arXiv · doi:10.48550/arxiv.1806.00582 · Cited by 2,077 · see citing works
Deep learning for sensor-based activity recognition: A survey
Wang, J., Chen, Y., Hao, S., Peng, X., & Hu, L. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.02.010 · Cited by 2,014 · cited 1× · see citing works
“State-of-the-art approaches rely on deep neural network models to replace traditional manual feature engineering and have greatly improved the accuracy of HAR [33, 34].”
MethodFederated Optimization: Distributed Machine Learning for On-Device Intelligence
Konečný, J., McMahan, H. B., Ramage, D., & Richtárik, P. (2016) · arXiv · doi:10.48550/arxiv.1610.02527 · Cited by 1,563 · see citing works
On First-Order Meta-Learning Algorithms
Nichol, A., Achiam, J., & Schulman, J. (2018) · arXiv · doi:10.48550/arxiv.1803.02999 · Cited by 1,438 · see citing works
Federated Learning for Mobile Keyboard Prediction
Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., & Ramage, D. (2018) · arXiv · doi:10.48550/arxiv.1811.03604 · Cited by 1,148 · see citing works
Meta-SGD: Learning to Learn Quickly for Few-Shot LearningPreprint
Li, Z., Zhou, F., Chen, F., & Li, H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.09835 · Cited by 1,026 · see citing works
Nweke, H. F., Teh, Y. W., Al-garadi, M. A., & Alo, U. R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.056 · Cited by 930 · see citing works
Learning Differentially Private Recurrent Language Models
McMahan, H. B., Ramage, D., Talwar, K., & Zhang, L. (2017) · arXiv · doi:10.48550/arxiv.1710.06963 · Cited by 849 · see citing works
Stisen, A., Blunck, H., Bhattacharya, S., Prentow, T. S., Kjærgaard, M. B., Dey, A., Sonne, T., & Jensen, M. M. (2015) · Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems · doi:10.1145/2809695.2809718 · Cited by 714 · see citing works
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing
Yao, S., Hu, S., Zhao, Y., Zhang, A., & Abdelzaher, T. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052577 · Cited by 667 · see citing works
USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors
Zhang, M., & Sawchuk, A. A. (2012) · Proceedings of the 2012 ACM Conference on Ubiquitous Computing · doi:10.1145/2370216.2370438 · Cited by 554 · see citing works
How the GDPR Will Change the World
Albrecht, J. P. (2016) · European Data Protection Law Review · doi:10.21552/edpl/2016/3/4 · Cited by 394 · see citing works
Improving Federated Learning Personalization via Model Agnostic Meta Learning
Jiang, Y., Konečný, J., Rush, K., & Kannan, S. (2019) · arXiv · doi:10.48550/arxiv.1909.12488 · Cited by 381 · cited 7× · see citing works
“Motivated by [10] which points out that FedAvg is a special case of Reptile, a scalable first-order meta-learning algorithm, we adopt a federated version of Reptile [21], which we call Federated Reptile (FedReptile), to meta-train the embedding network Θ in a federated manner among decentralized users.”
BackgroundMethodInfluentialLearning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks
Schmidhuber, J. (1992) · Neural Computation · doi:10.1162/neco.1992.4.1.131 · Cited by 357 · see citing works
Lane, N. D., Georgiev, P., & Qendro, L. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2750858.2804262 · Cited by 310 · see citing works
Yao, S., Zhao, Y., Zhang, A., Su, L., & Abdelzaher, T. (2017) · Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems · doi:10.1145/3131672.3131675 · Cited by 188 · see citing works
From smart to deep: Robust activity recognition on smartwatches using deep learning
Bhattacharya, S., & Lane, N. D. (2016) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2016.7457169 · Cited by 175 · see citing works
Learning to OptimizePreprint
Li, K., & Malik, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.01885 · Cited by 159 · see citing works
Towards multimodal deep learning for activity recognition on mobile devices
Radu, V., Lane, N. D., Bhattacharya, S., Mascolo, C., Marina, M. K., & Kawsar, F. (2016) · Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct · doi:10.1145/2968219.2971461 · Cited by 147 · see citing works
Flagged first, then by citations
Research works printed but not linked: 13
- [2][2] Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas. 2016. Learning to learn by gradient descent by gradient descent. In Advances in neural information processing systems . 3981-3989. -
- [3][3] Samy Bengio, Yoshua Bengio, Jocelyn Cloutier, and Jan Gecsei. 1992. On the optimization of a synaptic learning rule. In Preprints Conf. Optimality in Artificial and Biological Neural Networks . Univ. of Texas, 6-8. -
- [7][7] Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic metalearning for fast adaptation of deep networks. In Proceedings of the 34th International Conference on Machine Learning-Volume 70 . JMLR. org, 1126-1135. -
- [8][8] Matteo Gadaleta and Michele Rossi. 2016. Idnet: Smartphone-based gait recognition with convolutional neural networks. arXiv preprint arXiv:1606.03238 (2016). - · arXiv
- [13][13] Nicholas D Lane, Sourav Bhattacharya, Petko Georgiev, Claudio Forlivesi, Lei Jiao, Lorena Qendro, and Fahim Kawsar. 2016. Deepx: A software accelerator for low-power deep learning inference on mobile devices. In Proceedings of the 15th International Conference on Information Processing in Sensor Networks . IEEE Press, 23. -
- [14][14] Nicholas D Lane, Petko Georgiev, and Lorena Qendro. 2015. DeepEar: robust smartphone audio sensing in unconstrained acoustic environments using deep learning. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing . ACM, 283-294. -
- [17][17] Robert LiKamWa, Yunhui Hou, Julian Gao, Mia Polansky, and Lin Zhong. 2016. RedEye: analog ConvNet image sensor architecture for continuous mobile vision. In ACM SIGARCH Computer Architecture News , Vol. 44. IEEE Press, 255-266. -
- [20][20] Devang K Naik and RJ Mammone. 1992. Meta-neural networks that learn by learning. In [Proceedings 1992] IJCNN International Joint Conference on Neural Networks , Vol. 1. IEEE, 437-442. -
- [25][25] Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek. 2019. Robust and communication-efficient federated learning from non-iid data. arXiv preprint arXiv:1903.02891 (2019). - · arXiv
- [27][27] Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar. 2017. Federated multi-task learning. In Advances in Neural Information Processing Systems . 4424-4434. -
- [28][28] Konstantin Sozinov, Vladimir Vlassov, and Sarunas Girdzijauskas. 2018. Human Activity Recognition Using Federated Learning. In 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom) . IEEE, 1103-1111. -
- [30][30] Sebastian Thrun and Lorien Pratt. 2012. Learning to learn . Springer Science & Business Media. -
- [32][32] Hao Wang, Zakhary Kaplan, Di Niu, and Baochun Li. 2020. Optimizing Federated Learning on Non-IID Data with Reinforcement Learning. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications . IEEE, 1698-1707. -
Cited by
Sorted by citations
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022) · Sensors · doi:10.3390/s22041476 · Cited by 625 · see citing works
“Federated learning is a trending approach to resolve privacy issue in learning problems [45, 167, 192, 259].”
BackgroundHuman activity recognition using wearable sensors by heterogeneous convolutional neural networks
Han, C., Zhang, L., Tang, Y., Huang, W., Min, F., & He, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116764 · Cited by 130 · see citing works
ProtoHAR: Prototype Guided Personalized Federated Learning for Human Activity Recognition
Cheng, D., Zhang, L., Bu, C., Wang, X., Wu, H., & Song, A. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3275438 · Cited by 92 · see citing works
Federated Contrastive Learning With Feature-Based Distillation for Human Activity Recognition
Xiao, Z., & Tong, H. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3510428 · Cited by 86 · see citing works
Practically Adopting Human Activity Recognition
Xu, H., Zhou, P., Tan, R., & Li, M. (2023) · Proceedings of the 29th Annual International Conference on Mobile Computing and Networking · doi:10.1145/3570361.3613299 · Cited by 55 · see citing works
Qin, X., Wang, J., Ma, S., Lu, W., Zhu, Y., Xie, X., & Chen, Y. (2023) · Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3580305.3599360 · Cited by 29 · see citing works
- Authors
- Li, C., Niu, D., Jiang, B., Zuo, X., & Yang, J.
- Affiliations
- University of Alberta (CA)
- Tencent (China) (CN)
- Venue
- Proceedings of the Web Conference 2021
- Publisher
- ACM
- Published
- 2021-04-19
- Pages
- 912-922
- OpenAlex
- W3153149826
- Cited by
- 133
- References
- 23
- Citations by year
- 2026: 15 · 2025: 39 · 2024: 31 · 2023: 30 · 2022: 13 · 2021: 5
- FWCI
- 9.13
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International World Wide Web Conference (WWW) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- human activity recognition, federated learning, representation learning, meta-learning, model-agnostic meta-learning, personalized classification, mobile sensors, user heterogeneity, privacy-preserving machine learning, cross-user generalization
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