CEFEEL: Communication-Efficient FEderated Learning for Personal Assistant Applications

Smartphones have rapidly become ubiquitous with more than five billion users worldwide. Equipped with advanced sensors such as GPS, cameras, and microphones, they generate large amounts of diverse data. Much of this data, including locations, call logs, images, and online activities, is private. A suitable model training architecture is required to enhance usability through intelligent applications while preserving data privacy and security. In this article, we develop a communication-efficient federated learning (CEFEEL) framework for smartphone personal assistant applications to protect data privacy and maintain user experience with low communication and computational overhead. The proposed framework identifies parameters that have converged early in the training phase and hence can be frozen or communicated intermittently, reducing communication overhead without compromising model accuracy. Extensive experiments demonstrate the robustness of our approach against unbalanced and non-IID data distributions. The experiments show that our developed framework, named FedFreeze and FedFreeze+ techniques, outperforms state-of-the-art FL algorithms like FedAvg in reducing communication and computational costs, preserving privacy, and maintaining training efficiency with comparable accuracy.