  Sixth-Generation (6G)-based Internet of Everything applications (e.g.
autonomous driving cars) have witnessed a remarkable interest. Autonomous
driving cars using federated learning (FL) has the ability to enable different
smart services. Although FL implements distributed machine learning model
training without the requirement to move the data of devices to a centralized
server, it its own implementation challenges such as robustness, centralized
server security, communication resources constraints, and privacy leakage due
to the capability of a malicious aggregation server to infer sensitive
information of end-devices. To address the aforementioned limitations, a
dispersed federated learning (DFL) framework for autonomous driving cars is
proposed to offer robust, communication resource-efficient, and privacy-aware
learning. A mixed-integer non-linear (MINLP) optimization problem is formulated
to jointly minimize the loss in federated learning model accuracy due to packet
errors and transmission latency. Due to the NP-hard and non-convex nature of
the formulated MINLP problem, we propose the Block Successive Upper-bound
Minimization (BSUM) based solution. Furthermore, the performance comparison of
the proposed scheme with three baseline schemes has been carried out. Extensive
numerical results are provided to show the validity of the proposed BSUM-based
scheme.
