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As deep learning models become increasingly large, they pose significantchallenges in heterogeneous devices environments. The size of deep learningmodels makes it difficult to deploy them on low-power or resource-constraineddevices, leading to long inference times and high energy consumption. Toaddress these challenges, we propose FlexTrain, a framework that accommodatesthe diverse storage and computational resources available on different devicesduring the training phase. FlexTrain enables efficient deployment of deeplearning models, while respecting device constraints, minimizing communicationcosts, and ensuring seamless integration with diverse devices. We demonstratethe effectiveness of FlexTrain on the CIFAR-100 dataset, where a single globalmodel trained with FlexTrain can be easily deployed on heterogeneous devices,saving training time and energy consumption. We also extend FlexTrain to thefederated learning setting, showing that our approach outperforms standardfederated learning benchmarks on both CIFAR-10 and CIFAR-100 datasets.

FlexTrain: A Dynamic Training Framework for Heterogeneous Devices Environments