## 3 The Proposed FED-XAI Framework for QoE Predictions in V2X Environments
### Details of the Proposed FED-XAI Framework  
We consider an application where several instances of vehicular User Equipment (UE) connected to a B5G/6G Base Station (BS) receive a video stream whose quality plays a decisive role in the safety of remote driving. The quality of experience (QoE) perceived by UEs depends on the QoS provided by the network. This can be mapped, for instance, toa see-through use-case, where the receiving UE is a car using a live feed from the camera of another car (e.g., to make overtaking safer in the presence of visual impairments for the driver), or to ToD, where the sender is the car being driven and the receiver is the driving operator. In either case, operations may be supported by a DT of the traffic area at the edge. From an implementation perspective, Multi-access Edge Computing (MEC) infrastructure can be leveraged as an intrinsic facilitator for the segregation of data, as requested by international regulation in the matter of ITS services. Furthermore, state-of-the-art MEC technologies are defined by considering MEC security with an end-to-end approach, leveraging existing standards relevant in the area, e.g., ETSI-NFV-SEC (European Telecommunications Standards Institute--Network Functions Virtualisation--Security), TC CYBER (Technical Committee Cybersecurity), 3 GPP (Third Generation Partnership Project), carefully selected to be applicable in edge computing systems. The objective of the envisioned application is to employ XAI models, learnt (and updated) in a federated fashion based on QoS/QoE data, to predict the QoE perceived by UEs in the near future. Notably, it has been recently shown that highly interpretable tree-based models are able to achieve competitive performance in this specific task [21]. In the following, we describe how the FED-XAI approach can be deployed on a MEC-enabled B5G/6G architecture (see Figure 2). A FED-XAI computation engine (CE) instantiated within an edge/cloud node (also called a MEC host) is the central entity responsible for model aggregation. With reference to the see-through scenario in Figure 2, each UE measures QoS and QoE metrics, while receiving the video stream, possibly enhanced by the DT. Examples of relevant metrics are: received throughput, jitter, packet error rate (QoS), startup delay, number of stall events and rebuffering ratio (QoE). The collected values of such QoS/QoE metrics are securely transmitted by each (vehicular) UE to a corresponding MEC application, called FED-XAI manager (FM), possibly together with any other relevant information for the FED-XAI learning algorithm. Each UE communicates with its dedicated FM, which acts as an interface to the CE in the MEC system and provides all the functionalities to allow the UE to participate in the federation, e.g., join/leave, transmit/obtain model to/from the CE, etc. Alternatively, an FM can be hosted directly at the UE (instead of the MEC host). Figure 2: Example of video flow (red arrows) and related QoS/QoE metrics reporting (dashed black arrows) in a MEC-enabled FED-XAI architecture. Interaction among FM, CE and a real time XAI dashboard is also shown. We divide time in periods, as shown in Figure 3. During each period, a UE measures both QoS and QoE metrics. We call \(\mathbf{QOS}_{k}(i)\) and \(\mathbf{QOE}_{k}(i)\) the vectors of QoS and QoE metrics measured by \(UE_{k}\) during period \(i\). Both vectors are sent to the FM of \(UE_{k}\), \(FM_{k}\). At \(t=n\), \(FM_{k}\) uses the XAI model obtained by the CE to predict the QoE that \(UE_{k}\) will perceive in the next period, i.e., \(QOE_{k}(n+1)\). For building (or updating) the FED-XAI model, the involved FMs train (or update) the local model based on recent data (\(\mathbf{QOS}(i)\) and \(\mathbf{QOE}(i)\) for each \(i=n-m,n-m+1,\ldots,n\), where \(m\) is a predefined time window), and share it with the CE. Once the CE produces the aggregated FED-XAI model, the latter is sent back to the FMs that will use it to perform the QoE prediction for their corresponding UE.