Vehicle Selection for C-V2X Mode 4 Based Federated Edge Learning Systems

TL;DR

This work tackles FEEL in vehicular networks by addressing cache queue stability under C-V2X Mode 4. It introduces a Lyapunov optimization-based vehicle selection framework that jointly accounts for remaining data, transmission delay, collision probability, and survival time to maximize learning accuracy while preventing queue overflow. A two-tier RSU selection mechanism prioritizes vehicles by system status, achieving higher data uplift and faster convergence than baseline strategies. The results demonstrate practical viability for privacy-preserving, edge-assisted learning in dynamic vehicular environments with constrained RSU resources.

Abstract

Federated learning (FL) is a promising technology for vehicular networks to protect vehicles' privacy in Internet of Vehicles (IoV). Vehicles with limited computation capacity may face a large computational burden associated with FL. Federated edge learning (FEEL) systems are introduced to solve such a problem. In FEEL systems, vehicles adopt the cellular-vehicle to everything (C-V2X) mode 4 to upload encrypted data to road side units' (RSUs)' cache queue. Then RSUs train the data transmitted by vehicles, update the locally model hyperparameters and send back results to vehicles, thus vehicles' computational burden can be released. However, each RSU has limited cache queue. To maintain the stability of cache queue and maximize the accuracy of model, it is essential to select appropriate vehicles to upload data. The vehicle selection method for FEEL systems faces challenges due to the random departure of data from the cache queue caused by the stochastic channel and the different system status of vehicles, such as remaining data amount, transmission delay, packet collision probability and survival ability. This paper proposes a vehicle selection method for FEEL systems that aims to maximize the accuracy of model while keeping the cache queue stable. Extensive simulation experiments demonstrate that our proposed method outperforms other baseline selection methods.

Figures (13)

• Figure 1: System model.
• Figure 2: SPS process.
• Figure 3: Cache queue of RSU.
• Figure 4: RSU selection process for vehicles.
• Figure 5: Expected accuracy.