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Abstract Securing the connected vehicle is no longer optional; it is a prerequisite for safe, trustworthy mobility in the era of electric and autonomous transportation. Keywords: Sustainable City, Electric Mobility, Intrusion Detection, Federated Graph Neural Networks, Centrality measures The rapid growth of electric vehicles is accompanied by an increasing interconnection between vehicles, charging stations, digital platforms, supervision systems, and energy management devices. This evolution fosters the emergence of interoperable and data-driven smart mobility services, but it also exposes the entire ecosystem to growing cyber risks. In these distributed environments, where multiple stakeholders cooperate, security, resilience, and trust become essential prerequisites for the large-scale deployment of connected electric mobility. In complement to the eTruckCharge project, which aims to develop a federated charging network and associated intelligent services, this doctoral project addresses a closely related scientific challenge: the collaborative detection of attacks and anomalies in connected electric mobility ecosystems. The objective is to design mechanisms capable of coping with a wide variety of threats, particularly distributed, coordinated, or emerging attacks that are difficult to identify using traditional signature-based approaches. Although deep learning approaches have shown strong potential for intrusion detection, their centralized training raises significant limitations in terms of data privacy, data governance, robustness, and scalability. This thesis therefore proposes a distributed detection framework based on federated learning and Graph Neural Networks (GNNs), capable of modeling structural dependencies between the different components of an electric mobility system while keeping data locally at the level of the relevant stakeholders. Local models will be trained at the level of charging stations, vehicles, or edge nodes, and then aggregated collaboratively without any direct exchange of raw data. The originality of this work lies in the combined use of federated learning, GNNs, and centrality measures from complex network theory to improve anomaly detection, enhance robustness in heterogeneous environments, and increase generalization to unseen or weakly represented attacks in the training data. Particular attention will be paid to detecting new attack patterns without explicit signatures, by analyzing topological dependencies, relational behaviors, and structural disruptions in interaction graphs. Finally, in collaboration with ChargeMap, an innovative company based in Strasbourg and specialized in digital services for electric mobility, the approach will be evaluated through realistic scenarios related to charging infrastructures and associated services. The ambition is to propose a generic, distributed, and privacy-preserving methodological framework to strengthen the cybersecurity and resilience of future connected electric mobility systems. Research Work Scientific context   The rapid growth of connected and electric vehicles is accompanied by an increasing interconnection between embedded cyber-physical systems, external communication interfaces, digital platforms, and energy management devices. While this evolution enables the emergence of new intelligent and interoperable mobility services, it also significantly expands the cyber attack surface of the entire automotive ecosystem [1,2]. In such distributed environments, where multiple stakeholders collaborate without necessarily sharing their data or infrastructures, security, resilience, and trust become critical enablers for the large-scale deployment of connected electric mobility. Network Intrusion Detection Systems (NIDS) have emerged as an essential countermeasure for monitoring CPS traffic and detecting malicious activity [3]. Although deep learning-based NIDS have demonstrated promising detection performance, their centralized