## 4 Impacts of the Proposed FED-XAI Approach on V2X Applications in B5G/6G Networks  
The practical use value (or benefit) of FL for the considered V2X use cases, involving various local models instantiated at vehicles, roadside units and edge cloud infrastructure is that the QoE predictions issued by the resulting global aggregated model will be characterized by a higher generalization capability in space and in time than local models. In other words, when a QoE prediction request is issued by a (new) vehicle entering the area of interest, the QoE prediction value to be returned in response will be more accurate and of higher confidence than the one generated by a local model trained by using only the data acquired in the vehicle. On top of the increased generalization capability, a second benefit of the FED-XAI approach is the increase in trust in AI for 6G-enabled services. This has an immediate business impact on 6G business entities. This increase in trust is beneficial for all system entities, from end users to operators and service providers, edge-computing providers and other vertical market players (e.g., automotive, industrial automation, etc. ), as it better instils collaboration, starting from a business level. For instance, the exemplary V2X applications described in Section 3.1 are typical cases where a collaboration (and related business agreement) is needed between MNOs, possibly in partnership with edge-computing service providers and car OEMs. Both car OEMs and MNOs can benefit from explanations about such predictions and any consequent decision making: MNOs can provide a more explainable set of 6G functionalities (e.g., FL agents enabling QoE  
Figure 5: MDT georeferenced data from TIM live network on the area of Mestre and Venice cities. (**a**) MDT sample density (MDT sample/m\({}^{2}\)) in the area of Mestre and Venice Cities (Italy) averaged over 24 h. On the map, each single visible point is a square pixel with 1 m side. (**b**) MDT sample density on Venice city. (**c**) MDT Best Serving Cell radio coverage of Venice Cities (Italy). Each colour identifies a single cell in the 1800 MHz frequency band. On the map, each single visible point is a square pixel with 1 m side. predictions) and expose them to their customers (including car OEMs but also application developers and system integrators); OEMs can also benefit from more information on network predictions, exploitable to improve automated driving features offered to their end customers (i.e., the actual drivers). Figure 6 shows the same V2X service scenario, characterized by a fundamentally different view, depending on the perspective considered: a car dashboard (from the point of view of the user) shows the set of enabled V2X functionalities and their respective space and time availability. Instead, the view of a network operator (providing the needed communication and computation infrastructure) embeds more complexity, including the management and operation of the network infrastructure and the FED-XAI functionalities needed to provide QoE predictions for the offered V2X services. The boundary between these two worlds is typically governed by a set of Service Level Requirements (SLRs), defining the terms and conditions of the agreement between these two stakeholders (see 5 GAA reports for the V2X cases [2]). These SLRs are service-specific and can be defined in terms of minimum throughput, maximum delay, but also availability and reliability of the guaranteed KPIs (defined, e.g., in a certain time window). In this perspective, moving towards 6G, accurate and timely predictions (supported by XAI models) are key to providing advanced and very demanding use cases, with a horizon ranging from extremely short to long time windows. Therefore, it is evident how FED-XAI is paramount for improving the understanding and mutual trust among 6G business entities (i.e., MNOs and OEMs here).