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Abstract AI is revolutionizing our society promising unmatched efficiency and effectiveness in numerous tasks. It is already exhibiting remarkable performance in several fields, from smartphones’ cameras to smart grids, from finance to medicine, to name but a few. Given the increasing reliance of applications, services, and infrastructures on AI models, it is fundamental to protect these models from malicious adversaries. On the one hand, AI models are black boxes whose behavior is unclear and depends on training data. On the other hand, an adversary can render an AI model unusable with just a few specially crafted inputs, driving the model’s predictions according to her desires. This threat is especially relevant to collaborative protocols for AI models training and inference. These protocols may involve participants whose trustworthiness is uncertain, raising concerns about insider attacks to data, parameters, and models. These attacks ultimately endanger humans, as AI models power smart services in real life (AI-based IoT). A key need emerges: ensuring that AI models and, more generally, AI-based systems trained and operating in a low-trust environment can guarantee a given set of non-functional requirements, including cybersecurity-related ones . Our paper targets this need, focusing on collaborative drone swarm missions in hostile environments. We propose a methodology that supports trustworthy data circulation and AI training among different, possibly untrusted, organizations involved in collaborative drone swarm missions. This methodology aims to strengthen collaborative training, possibly built on incremental and federated learning.