## 2 FED-XAI: Bringing together Federated Learning and Explainable AI  
Existing AI-based solutions for wireless network planning, design and operation ignore either or both of the following aspects: (i) the need to preserve data privacy at all times, including wireless transfer and storage, and (ii) the explainability of the involved models. Furthermore, latency and reliability requirements of safety-critical automotive communications call for seamless availability of decentralized and lightweight intelligence, where data are generated--and decisions made--anytime and anywhere. Current FL approaches only address the first requirement. Explainability has been given less attention, having been approached primarily by exploiting post-hoc techniques, e.g., Shapley values to measure feature importance [12]. There is a substantial lack of approaches for FL of inherently explainable models. On the other hand, a federated approach for learning interpretable-by-design models, in which transparency is guaranteed for every decision made, would represent a significant leap towards trustworthy AI. Therefore, we introduce the concept of FL of XAI (FED-XAI) models, as a framework with a twofold objective: first, to leverage FL for privacy preservation during collaborative training of AI models, especially suitable in heterogeneous B5G/6G scenarios; second, to ensure an adequate degree of explainability of the models themselves (including the obtained aggregated model as a result of FL). In the following, we provide some insights into how inherently explainable models (e.g., decision trees or rule-based) can be learned, employing an FL paradigm. First, it is worth noting that standard algorithms for learning such models typically adopt a heuristic approach; in fact, gradient descent-based optimization methods, widely used in FL, cannot be immediately applied, as they require the formulation of a global objective function. The greedy induction of decision trees, for example, recursively partitions the feature space by selecting for each decision node the most suitable attribute. The major challenge of the FED-XAI approach, therefore, consists in generating XAI models, whose FL is not based on the optimization of a differentiable global objective function. The proposed FED-XAI approach relies on orchestration by a central entity but ensures that local data are not exposed beyond source devices: each data owner learns a model by elaborating locally acquired raw data and shares such a model with the central server, which merges the received models to produce a global model (Figure 1). Notably, our envisioned approach for federated learning of explainable AI models ensures data privacy regardless of the data sample size. As per the advantages of the FED-XAI approach, we expect that the global aggregated model performs better than the local models because it exploits the overall information stored and managed by all data owners, without compromising model interpretability. Figure 1: Illustration of federated learning of XAI models. Our approach differs from classical FL in two aspects: first, it entails a one-shot communication scheme for each model update and not an iterative algorithm. As a consequence, the communication overhead is reduced, and the system is more robust to possible connectivity problems. Second, merging decision trees and rule-based models requires defining appropriate procedures, necessarily different from the simple weighted average of models of the FedAvg protocol applied, for example, to NNs. In more detail, the XAI models we consider can be represented as collections of \"IF _antecedent_ THEN _consequent_\" rules, (natively in a rule-based system, and easily obtainable also from a decision tree). This representation is applicable regardless of the target task (regression or classification) and the type of the attributes (e.g., nominal or numeric).