A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks

arXiv:2308.10664v3 Announce Type: replace
Abstract: Progressing towards a new era of Artificial Intelligence (AI) - enabled wireless networks, concerns regarding the environmental impact of AI have been raised both in industry and academia. Federated Learning (FL) has emerged as a key privacy preserving decentralized AI technique. Despite efforts currently being made in FL, its environmental impact is still an open problem. Targeting the minimization of the overall energy consumption of an FL process, we propose the orchestration of computational and …

abstract academia artificial artificial intelligence arxiv communication concerns cs.ai cs.lg decentralized decentralized ai energy energy efficient environmental environmental impact federated learning impact industry intelligence key networks privacy privacy preserving reinforcement reinforcement learning type wireless