Martin Isaksson

• Master of Science
• Senior Researcher at Ericsson

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 optimal when data is heterogeneous and non-IID. We propose a practical and robust approach to personalization in FL that adjusts to heterogeneous and non-IID data by balancin...

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 optimal when data is heterogeneous and non-Independent and Identically Distributed (non-IID). We propose a practical and robust approach to personalization in FL that adjusts...

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 functions, which in turn may increase threats to sensitive end-user information. Consequently, mechanisms to reduce threats to end-user privacy are needed to take full advantage...

For controlling radio transmission in a cellular network, a network node (100) detects data traffic between the cellular network and a user equipment (10). The detected data traffic is based on a transport protocol which involves transmission of an acknowledgement to acknowledge successful reception of data by the user equipment (10). The network n...

A Wireless Sensor Network (WSN) of anisotropic magnetoresistor sensors offers a low-cost alter- native to other traffic measurement technologies. The WSNs offer better reliability than other solutions, they offer more information, can be deployed quickly and be reused. In this thesis the sensor algorithms used for detection, velocity estimation, qu...