## 1 Introduction  
Artificial Intelligence (AI), along with Machine Learning (ML) as one of its core building blocks, is entering many market domains at a fast pace and will not only leverage advanced communication networks but also shape the definition of next-generation networks themselves. In particular, AI is expected to play a crucial role in the design, operation and management of future beyond-5G (B5G)/6G networks and in a plethora of applications [1]. However, the introduction of in-network AI comes with growing concerns on privacy, security and trust for citizens and users; for this reason, the adoption of eXplainable AI (XAI) models is an emerging trend considered for the design of transparent AI-based solutions. Moreover, future service scenarios, especially in the automotive domain, will be characterized by the deployment of connected vehicular systems from heterogeneous car manufacturers, connected via different Mobile Network Operators (MNOs) and different technology infrastructures [2]. In such complex setups, it will be imperative for service providers to consider federated network environments including multiple administrative and technical domains as a working assumption for the design of innovative applications. It is worth noting that the automated driving use case of \"Teleoperated Driving (ToD) for Remote Steering\" [3] requires a throughput of up to 36 Mbps per single stream, along with a positioning accuracy of 0.1 m and a reliability of 99.999% for the service to be considered available to the end customer. Such stringent requirements call for new technical enablers, to be introduced as part of the 6G network design. Considering the above-mentioned challenges, in this article we envision the use of the federated learning (FL) concept applied jointly with XAI models and discuss its applicability to automated vehicle networking use cases to be encountered in B5G/6G setups. In fact, although FL has recently been widely investigated in the context of Neural Networks and Deep Learning models (due to their gradient based optimization strategy), much less attention has been devoted so far to FL of XAI models.  
The main contributions of this article can be summarized as follows:  
* We propose the integration of FL with XAI for performing quality of experience (QoE) predictions in B5G/6G networks, by providing a detailed discussion about the benefits it can bring and the main challenges that need to be addressed;
* Considering vehicle-to-everything (V2X) applications as relevant use cases, we present the design of a framework to evaluate the benefits of the proposed approach and provide the guidelines to implement a realistic B5G/6G network testbed supporting the training of XAI models in a federated fashion, as well as the issuance of explainable QoE predictions;
* We shed light on the impact that the proposed FL approach with XAI models will have on both the industrial and standardization sectors.  
While the following subsections provide an overview of XAI and FL, respectively, Section 2 describes the FL of XAI models applied to advanced 5G systems towards 6G. Section 3 elaborates on some relevant V2X use cases and provides more details on the proposed FED-XAI framework, focusing on a QoE prediction task. In Section 4, the impact of such solutions to the automotive vertical segment is discussed: in particular, the benefits of predicted QoE explanations useful for decision making are detailed for both car Original Equipment Manufacturers (OEMs) and MNOs. Standardization impacts are also analysed to provide interoperable and globally applicable solutions, and some challenges of FL of XAI models are discussed. Finally, Section 5 draws some conclusions.