DISTINQT: A Distributed Privacy Aware Learning Framework for QoS Prediction for Future Mobile and Wireless Networks

no code implementations • 15 Jan 2024 • Nikolaos Koursioumpas, Lina Magoula, Ioannis Stavrakakis, Nancy Alonistioti, M. A. Gutierrez-Estevez, Ramin Khalili

Alternative solutions have been surfaced (e. g. Split Learning, Federated Learning), distributing AI tasks of reduced complexity across nodes, while preserving the privacy of the data.

Federated Learning

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

no code implementations • 21 Aug 2023 • Nikolaos Koursioumpas, Lina Magoula, Nikolaos Petropouleas, Alexandros-Ioannis Thanopoulos, Theodora Panagea, Nancy Alonistioti, M. A. Gutierrez-Estevez, Ramin Khalili

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 Privacy Preserving +1

A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks

no code implementations • 25 Jun 2023 • Lina Magoula, Nikolaos Koursioumpas, Alexandros-Ioannis Thanopoulos, Theodora Panagea, Nikolaos Petropouleas, M. A. Gutierrez-Estevez, Ramin Khalili

Federated Learning (FL) has emerged as a decentralized technique, where contrary to traditional centralized approaches, devices perform a model training in a collaborative manner, while preserving data privacy.

Federated Learning Total Energy

Hybrid Model and Data Driven Algorithm for Online Learning of Any-to-Any Path Loss Maps

no code implementations • 14 Jul 2021 • M. A. Gutierrez-Estevez, Martin Kasparick, Renato L. G. Cavalvante, Sławomir Stańczak

Pure data-driven methods can achieve good performance without assuming any physical model, but their complexity and their lack of robustness is not acceptable for many applications.