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Federated Learning on Wearable Devices

This is the implementation of the paper: Demo Abstract: Federated Learning on Wearable Devices.

Requirements

  • Java 11

Data

We setup our demo in a controlled environment with a Human Activity Recognition (HAR) dataset. The dataset is built from a Daily and Sports Activities Data Set (DSA), which comprises of motion sensor data of 19 daily sports activities each performed by 8 subjects in their own style for 5 minutes.

Model

The selected model is a MLP, composed of one input and one output layer, and one hidden layer with 1000 units using ReLU activations.

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Run

Results

Ackonwledgements

This work is sponsored by CERNET Innovation Project (NGII20190105) and Academy of Finland grant 325774 and 325570.

References

[1] Billur Barshan and Murat Cihan Yüksek. 2013. Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units. The Computer Journal 57, 11 (2013), 1649–1667.

[2] Y. Chen, X. Qin, J. Wang, C. Yu, and W. Gao. 2020. FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare. IEEE Intelligent Systems 35, 4 (2020), 83–93.

[3] H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics.

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