## 2 FED-XAI: Bringing together Federated Learning and Explainable AI
The aggregation procedure consists in juxtaposing rules collected from data owners, and resolving possible _conflicts_, which emerge when rules from different models, having antecedents referring to identical or overlapping regions of the attribute space, have different consequents. In one of our recent works [13], we presented a novel approach for FL of Takagi-Sugeno-Kang (TSK) fuzzy rule based systems [14], which can be considered as XAI models in regression problems. In a TSK model, the antecedent of a rule identifies a specific region of the attribute space, whereas the corresponding consequent allows for the evaluation of the predicted output within such a region as a linear combination of the input variables. When two rules, generated by different clients, share the same antecedent, the aggregation strategy for generating the FED-XAI model involves combining the two rules into a single one with the same antecedent: the coefficients of the linear model of the new consequent are evaluated as the weighted average of the coefficients of the original rules, where the weight of each rule depends on its support and confidence values. Research efforts in the FED-XAI domain, however, are still in their embryonic stage: as for tree-based models, a preliminary investigation of the trade-off between accuracy and interpretability has been recently carried out [15], but learning strategies compliant with the federated setting still need to be sharpened. The FED-XAI approach may find immediate applicability in the automated vehicle networking domain, and, specifically, within the exemplary scenario described in Section 1: a model for QoS prediction which is explainable by a wide set of service consumers may be learned in a federated manner, reaping the benefits of collaborative training and privacy protection. Main Challenges of the FED-XAI ApproachThere are also challenges related to the FED-XAI approach, especially for time-critical operations in automated driving setups. For example, the computation (and, therefore, energy) footprint of FED-XAI needs to be pre-evaluated before implementation to identify the scalability potential of the solution. A clear distinction should be made between the stages of _training_ and _inference_. For most ML models, including decision tree and rule-based systems, the inference time (critical from automated driving service standpoint) is negligible compared to the training time and, in any case, model complexity can be tuned to ensure that time constraints are satisfied. A larger computational overhead is required in the training stage, but it does not affect the application (e.g., learning can be performed in idle state). Another challenge is FED-XAI system resilience to attackers trying to benefit from the access to explanations of QoE predictions (e.g., towards increasing automated driving service outages for all or targeted vehicles). Finally, the approach will also need to address some additional challenges that are typical of FL and are likely to characterize 6G network-based intelligent transportation applications: (i) multi-source data may have different distributions and volumes, (ii) the number of participants can grow fast and their participation to FL may be unstable due to insufficiency of radio and computational resources, and (iii) learned models will need to be agilely updated in scenarios where concept drift alters the characteristics of data distributions over time.