An Application for Federated Learning of XAI Models in Edge Computing Environments

The next generation of wireless networks will feature an increasing number of connected devices, which will produce an unprecedented volume of data. Knowledge extraction from decentralized data imposes the exploitation of computing and learning paradigms able to tame the complexity of the network and meet the growing requirement of trustworthiness. In this regard, edge computing overcomes the limitations of cloud computing by moving virtualized computing and storage resources closer to data sources. Furthermore, Federated Learning has been recently proposed as a means to let multiple parties collaboratively train an ML model without disclosing private data. In this paper, we propose an application that enables Federated Learning of eXplainable AI models (Fed-XAI) in an edge computing environment. The proposal represents a step forward towards the adoption of trustworthy AI in next generation wireless networks, ensuring both privacy preservation and explainability. The application components are described, along with the workflow for the training and inference stages. Finally, we discuss the application deployment, in a simulated setting, for addressing a task of video streaming Quality of Experience forecasting in a vehicular network case study.