AceFL: Federated Learning Accelerating in 6G-enabled Mobile Edge Computing Networks – IEEE

Heterogeneity among distributed edge devices and the limitation of resources may degrade the training efficiency of Federated Edge Learning over 6G-enabled mobile edge computing (MEC) networks. Taking this challenge into account, a novel federated learning scheme is proposed in this paper to accelerate the training process.
Editorial for the Special Issue on 6G Requirements, Visions, and Enabling Technologies – Engineering
Decentralized federated learning for extended sensing in 6G connected vehicles – ScienceDirect
In this paper we investigate distributed federated learning (FL) for augmenting the capability of road user/object classification based on Lidar data. More specifically, we propose a new modular, decentralized approach, referred to as consensus-driven FL (C-FL), suitable for PointNet compliant deep machine learning architectures and Lidar point cloud processing for road actor classification.