QuaFedAsync: Quality-based Asynchronous Federated Learning for the Embedded Systems

Paper in proceeding, 2023

In recent years, Federated Learning, as an approach to distributed learning, has shown its potential with the increasing number of devices on the edge and the development of computing power. The method enables large-scale training on the device that creates the data but with the sensitive data remaining within the data's owner. In reality, however, the vast majority of enterprises have the problem of low data volume and poor model quality to support the implementation of Federated Learning methods. Learning quality assurance for edge devices is still the major issue which prevents Federated Learning to be applied in industrial contexts, especially in safety-critical applications. In this paper, we propose a quality-based asynchronous Federated Learning algorithm (QuaFedAsync) to address these challenges. We report on a study in which we used two well-known data sets, i.e., DDAD and KITTI datasets, and validate the proposed algorithm on an industrial use case concerned with monocular depth estimation in the automotive domain. Our results show that the proposed algorithm significantly improves the prediction performance compared to the commonly applied aggregation protocols while maintaining the same level of accuracy as centralized machine learning. Based on the results, we prove the learning efficiency and robustness when applying the algorithm to industrial scenarios.