An EMD-Based Adaptive Client Selection Algorithm for Federated Learning in Heterogeneous Data Scenarios - Chen, Aiguo; Fu, Yang; Sha, Zexin; Lu, Guoming (Frontiers in Plant Science, 2022)
Federated Learning is a distributed machine learning framework that enables distributed nodes with computation and store capability to train a global model while keeping distributed-stored data locally, and it can not only promote the efficiency of modeling but also preserve the privacy of data. Therefore, federated learning can be widely applied in distributed conjoint analysis scenarios, such as Smart Plant Protection systems, in which widely networked IoT devices are applied to monitor the critical data of plant production for the improvement of crop production. However, the data collected by different IoT devices can be non-independent and identically distributed (non-IID), causing the challenge of statistical heterogeneity, and it has been proved by recent research that statistical heterogeneity can lead to a significant decline in the efficiency of federated learning, making it hard to apply in practice. In order to promote the efficiency of federated learning in statistical heterogeneity scenarios, an adaptive client selection algorithm for federated learning in statistical heterogeneous scenarios called ACSFed is proposed in this paper. ACSFed can dynamically calculate the possibility of clients being selected to train the model for each epoch based on their local statistical heterogeneity and previous training performance instead of randomly selecting clients. Multiple experiments on public benchmark data sets reveal the improvements in the efficiency of the models in heterogeneous settings.