113 citations · 290 across the 22 of their papers we have counts for
5 papers · 1 filter
A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks
Nikolaos Koursioumpas, Lina Magoula, Nikolaos Petropouleas +5
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 a…
Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey
Kilian Pfeiffer, Martin Rapp, Ramin Khalili +1
With an increasing number of smart devices like internet of things (IoT) devices deployed in the field, offloadingtraining of neural networks (NNs) to a central server becomes more…
A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks
Lina Magoula, Nikolaos Koursioumpas, Alexandros-Ioannis Thanopoulos +4
Federated Learning (FL) has emerged as a decentralized technique, where contrary to traditional centralized approaches, devices perform a model training in a collaborative manner,…
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices
Kilian Pfeiffer, Ramin Khalili, Jörg Henkel
Federated learning (FL) is usually performed on resource-constrained edge devices, e.g., with limited memory for the computation. If the required memory to train a model exceeds th…
FedZero: Leveraging Renewable Excess Energy in Federated Learning
Philipp Wiesner, Ramin Khalili, Dennis Grinwald +3
Federated Learning (FL) is an emerging machine learning technique that enables distributed model training across data silos or edge devices without data sharing. Yet, FL inevitably…