Abstract
Federated learning (FL) is a distributed training technique for machine learning (ML) models that ensures ownership of training data for the devices or users. Data ownership is guaranteed when the devices train the machine model. The attribution of responsibility for the distributed training of the model causes variations in the training efficiency based on the characteristics or behaviors of these users. Among the user characteristics that can interfere with federated training is mobility. The mobility of users may prevent the user from completing the training by losing connection with other devices on the network, causing a client dropout. This work introduces a specific FL coordination algorithm to guarantee training efficiency in scenarios with mobility named MoFeL. To analyze its efficiency, we performed simulation experiments using machine models trained by a convolutional neural network from an image classification application. Simulation results show that MoFeL performs FL even in scenarios with intense user mobility, while other traditional training coordination algorithms cannot do so.
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Acknowledgements
The authors would like to thank Virtus - Research, Development and Innovation Center and Programa de Pós-Graduação em Engenharia Elétrica (COPELE), both from the Federal University of Campina Grande for supporting this research.
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Macedo, D., Santos, D., Perkusich, A. et al. A mobility-aware federated learning coordination algorithm. J Supercomput 79, 19049–19063 (2023). https://doi.org/10.1007/s11227-023-05372-3
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DOI: https://doi.org/10.1007/s11227-023-05372-3