2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.IT2023
mmWave Beam Selection in Analog Beamforming Using Personalized Federated Learning
Martin Isaksson, Filippo Vannella, David Sandberg +1
Using analog beamforming in mmWave frequency bands we can focus the energy towards a receiver to achieve high throughput. However, this requires the network to quickly find the bes…
cs.LG2022★ 2 cited
Adaptive Expert Models for Personalization in Federated Learning
Martin Isaksson, Edvin Listo Zec, Rickard Cöster +2
Federated Learning (FL) is a promising framework for distributed learning when data is private and sensitive. However, the state-of-the-art solutions in this framework are not opti…
cs.CR2020
Secure Federated Learning in 5G Mobile Networks
Martin Isaksson, Karl Norrman
Machine Learning (ML) is an important enabler for optimizing, securing and managing mobile networks. This leads to increased collection and processing of data from network function…