GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learning

The challenges of distributed computing and privacy preservation in the Internet of Things (IoT) can be effectively addressed through the federated learning (FL) paradigm. A pivotal aspect of this approach is client selection, which plays a crucial role in identifying participating clients while balancing model accuracy and communication efficiency. However, existing methods often have limitations, such as an inability to quickly and accurately assess data quality on the client side and a failure to account for the interdependence among clients by treating each as an independent contributor. To address these issues, we introduce GPFL, a novel framework for client selection in FL. We propose an indicator that rapidly and accurately measures the contribution of heterogeneous client data to FL. Additionally, we design an exploration–exploitation mechanism that selects the optimal combination of clients while considering their interdependencies. Both theoretical and experimental analyzes demonstrate the effectiveness of GPFL in improving FL efficiency and accuracy while preserving data privacy. Experiments on federated extended MNIST (FEMNIST) and CIFAR-10 show that GPFL outperforms baseline methods in Non-IID scenarios, achieving an average improvement of over 9% in FEMNIST accuracy compared to SOTA preselection methods. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://figshare.com/articles/dataset/PBFL/29264663</uri>.