Green AI Orchestration Bridging Trustworthy AI and Edge AI through tinyML for Frugal Intelligence

The rapid proliferation of edge AI, particularly within the TinyML ecosystem, demands innovative approaches to balance computational efficiency, energy consumption, and model performance. This paper explores the relevance of Green AI orchestration—exemplified by initiatives such as the RAIDO project—to the TinyML community, where resource constraints and sustainability are paramount. By integrating energy-efficient hardware, optimized algorithms, and adaptive orchestration frameworks, green AI paradigms allied to TinyML offer a path-way to deploy trustworthy, low-footprint AI models on resource-limited devices. We examine how these principles align with the ethos of frugal AI, which prioritizes minimalism, accessibility, and environmental responsibility. The paper sets the stage for a new era of Frugal Edge AI, where green TinyML orchestration not only reduces operational costs and carbon footprints but also democratizes AI deployment in edge environments. Our discussion underscores the synergy between green, frugal and edge AI, advocating for a holistic approach to AI development that harmonizes technological advancement with environmental stewardship.