Unsupervised Data Splitting Scheme for Federated Edge Learning in IoT Networks
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Federated Edge Learning (FEEL) is a promising distributed learning technique that aims to train a shared global model while reducing communication costs and promoting users' privacy. However, the training process might significantly occupy a long time due to the nature of the used data for training, which leads to higher energy consumption and therefore impacts the model convergence. To tackle this issue, we propose a data-driven federated edge learning scheme that tends to select suitable participating nodes based on quality data and energy. First, we design an unsupervised data-aware splitting scheme that partitions the node's local data into diverse samples used for training. We incorporate a similarity index to select quality data that enhances the training performance. Then, we propose a heuristic participating nodes selection scheme to minimize the communication and computation energy consumption, as well as the amount of communication rounds. The obtained results show that the proposed scheme substantially outperforms the vanilla FEEL in terms of energy consumption and the number of communication rounds.
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