## 1 Introduction
### Federated Learning  
Exploiting data from multiple sources can enhance the performance (i.e., high accuracy based on reduced bias) of AI models. However, wirelessly collecting and storing peripheral data for processing on a centralized server has become increasingly impractical due to two main reasons: first, it typically introduces severe communication and computation overhead due to the transmission and storage of large training data sets, respectively; second, it violates the privacy and security requirements imposed by data owners by expanding the surface of possible over-the-air attacks towards biased decision making. In other words, the preservation of data privacy represents an urgent requirement of today's AI/ML systems, because data owners are often reluctant to share their data with other parties; in some jurisdictions, users have the ability to consent or not with the sharing of privacy-sensitive data (e.g., per the General Data Protection Regulation--GDPR in European Union). Such a need to preserve privacy of data owners, however, clashes with the need to collect data to train accurate ML models, which are typically data hungry in their learning stage. To overcome these limitations, FL has been proposed as a privacy-preserving paradigm for collaboratively training AI models. In an FL system, participants iteratively learn a shared model by only transferring local model updates and receiving an aggregated shared model update, without sharing raw data.  
The main opportunities of FL in the context of Intelligent Transportation Systems (ITS) have been recently discussed in [9]: FL is expected to support both vehicle management (i.e., automated driving) and traffic management (i.e., infotainment and route planning) applications. Furthermore, FL has been applied in the context of Ultra-Reliable Low-Latency Communications for Vehicle-to-Vehicle scenarios, allowing vehicular users to estimate the distribution of extreme events (i.e., network-wide packet queue lengths exceeding a predefined threshold) with a model learned in a decentralized manner [10]. The model parameters are obtained by executing maximum likelihood estimation in a federated fashion, without sharing the local queue state information data. The concept of Federated Vehicular Network (FVN) has been recently introduced [11], as an architecture with decentralized components that natively support applications, such as entertainment at sport venues and distributed ML. However, FVN is a stationary vehicular network and relies on the assumption that vehicles remain at a fixed location, e.g., parking lots, so that the wireless connection is stable.  
In most of the work on FL, the strategy for model aggregation was inspired by the federated averaging protocol (FedAvg), which enables collaborative Stochastic Gradient Descent (SGD) optimization in a federated manner. Thus, FL has been extensively investigated for models implementing SGD as their optimization method, such as Neural Networks (NNs), but has not yet been adequately studied in the context of inherently explainable models. The following section introduces how XAI models can be generated by FL, a new approach which appears very promising for future 6G systems.