Dynamic Clustering in Federated Learning

no code implementations • 7 Dec 2020 • Yeongwoo Kim, Ezeddin Al Hakim, Johan Haraldson, Henrik Eriksson, José Mairton B. da Silva Jr., Carlo Fischione

To overcome these limitations, in this paper, we propose a three-phased data clustering algorithm, namely: generative adversarial network-based clustering, cluster calibration, and cluster division.

Clustering Federated Learning +4

Paving the Roadway for Safety of Automated Vehicles: An Empirical Study on Testing Challenges

no code implementations • 9 May 2017 • Alessia Knauss, Jan Schröder, Christian Berger, Henrik Eriksson

This paper presents current challenges of testing the functionality and safety of automated vehicles derived from conducting focus groups and interviews with 26 participants from five countries having a background related to testing automotive safety-related topics. We provide an overview of the state-of-practice of testing active safety features as well as challenges that needs to be addressed in the future to ensure safety for automated vehicles.