REPA: Client Clustering without Training and Data Labels for Improved Federated Learning in Non-IID Settings

no code implementations • 25 Sep 2023 • Boris Radovič, Veljko Pejović

Clustering clients into groups that exhibit relatively homogeneous data distributions represents one of the major means of improving the performance of federated learning (FL) in non-independent and identically distributed (non-IID) data settings.

Clustering Federated Learning

Mobiprox: Supporting Dynamic Approximate Computing on Mobiles

no code implementations • 16 Mar 2023 • Matevž Fabjančič, Octavian Machidon, Hashim Sharif, Yifan Zhao, Saša Misailović, Veljko Pejović

Runtime-tunable context-dependent network compression would make mobile deep learning (DL) adaptable to often varying resource availability, input "difficulty", or user needs.

Human Activity Recognition