Decentralized federated learning for extended sensing in 6G connected vehicles

Research on smart connected vehicles has recently targeted the integration of vehicle-to-everything (V2X) networks with Machine Learning (ML) tools and distributed decision making. Among these convergent paradigms, Federated Learning (FL) allows the vehicles to train a deep ML model collaboratively, by exchanging model parameters (i.e., neural network weights and biases), rather than raw sensor data, via V2X links. Early FL approaches resorted to a server-client architecture, where a Parameter Server (PS) acts as edge device to orchestrate the learning process. Novel FL tools, on the other hand, target fog architectures where the model parameters are mutually shared by vehicles and synchronized in a distributed manner via consensus algorithms. These tools do not rely on the PS, but take advantage of low-latency V2X links. In line with this recent research direction, in this paper we investigate distributed FL methods for augmenting the capability of road user/object classification based on Lidar data. More specifically, we propose a new modular, decentralized approach to FL, referred to as consensus-driven FL (C-FL), suitable for PointNet compliant deep ML architectures and Lidar point cloud processing for road actor classification. The C-FL process is evaluated by simulating a realistic V2X network, based on the Collective Perception Service (CPS), for mutual sharing of the PointNet model parameters. The performance validation considers the impact of the degree of connectivity of the vehicular network, the benefits of continual learning over heterogeneous training data, convergence time and loss/accuracy tradeoffs. Experimental results indicate that C-FL complies with the extended sensors use cases for high levels of driving automation, it provides a low-latency training service, compared with existing distributed ML approaches, and it outperforms ego learning with minimal bandwidth usage.