Ubiquitous wearable and mobile devices provide access to a diverse set of data. However, the mobility demand for our devices naturally imposes constraints on their computational and communication capabilities. A solution is to locally learn knowledge from data captured by ubiquitous devices, rather than to store and transmit the data in its original form. In this paper, we develop a federated learning framework, called Centaur, to incorporate on-device data selection at the edge, which allows partition-based training of a deep neural nets through collaboration between constrained and resourceful devices within the multidevice ecosystem of the same user. We benchmark on five neural net architecture and six datasets that include image data and wearable sensor time series. On average, Centaur achieves ~19% higher classification accuracy and ~58% lower federated training latency, compared to the baseline. We also evaluate Centaur when dealing with imbalanced non-iid data, client participation heterogeneity, and different mobility patterns. To encourage further research in this area, we release our code atthis https URL
View on arXiv@article{mo2025_2211.04175,
title={ Enhancing Efficiency in Multidevice Federated Learning through Data Selection },
author={ Fan Mo and Mohammad Malekzadeh and Soumyajit Chatterjee and Fahim Kawsar and Akhil Mathur },
journal={arXiv preprint arXiv:2211.04175},
year={ 2025 }
}