Green Federated Learning: A new era of Green Aware AI

The development of AI applications, especially in large-scale wirelessnetworks, is growing exponentially, alongside the size and complexity of thearchitectures used. Particularly, machine learning is acknowledged as one oftoday's most energy-intensive computational applications, posing a significantchallenge to the environmental sustainability of next-generation intelligentsystems. Achieving environmental sustainability entails ensuring that every AIalgorithm is designed with sustainability in mind, integrating greenconsiderations from the architectural phase onwards. Recently, FederatedLearning (FL), with its distributed nature, presents new opportunities toaddress this need. Hence, it's imperative to elucidate the potential andchallenges stemming from recent FL advancements and their implications forsustainability. Moreover, it's crucial to furnish researchers, stakeholders,and interested parties with a roadmap to navigate and understand existingefforts and gaps in green-aware AI algorithms. This survey primarily aims toachieve this objective by identifying and analyzing over a hundred FL works,assessing their contributions to green-aware artificial intelligence forsustainable environments, with a specific focus on IoT research. It delves intocurrent issues in green federated learning from an energy-efficient standpoint,discussing potential challenges and future prospects for green IoT applicationresearch.