Federated Data-Driven Kalman Filtering for State Estimation
This addresses localization challenges for autonomous vehicles by enabling collaborative training without heavy communication overhead, though it is incremental as it builds on existing KalmanNet methods.
The paper tackles the problem of accurate localization for autonomous vehicles by proposing FedKalmanNet, a federated learning framework that trains a neural network to estimate Kalman filter uncertainties without real-time communication, achieving superior performance over state-of-the-art collaborative decision-making approaches in simulations.
This paper proposes a novel localization framework based on collaborative training or federated learning paradigm, for highly accurate localization of autonomous vehicles. More specifically, we build on the standard approach of KalmanNet, a recurrent neural network aiming to estimate the underlying system uncertainty of traditional Extended Kalman Filtering, and reformulate it by the adapt-then-combine concept to FedKalmanNet. The latter is trained in a distributed manner by a group of vehicles (or clients), with local training datasets consisting of vehicular location and velocity measurements, through a global server aggregation operation. The FedKalmanNet is then used by each vehicle to localize itself, by estimating the associated system uncertainty matrices (i.e, Kalman gain). Our aim is to actually demonstrate the benefits of collaborative training for state estimation in autonomous driving, over collaborative decision-making which requires rich V2X communication resources for measurement exchange and sensor fusion under real-time constraints. An extensive experimental and evaluation study conducted in CARLA autonomous driving simulator highlights the superior performance of FedKalmanNet over state-of-the-art collaborative decision-making approaches, in localizing vehicles without the need of real-time V2X communication.