USFCF: Unified State-Space Feedback Control Framework for Robust Privacy-Conscious Federated Learning in Vehicular Service Networks

In the Internet of Vehicles (IoV), safeguarding service privacy and communication efficiency is critical due to the vast exchange of sensitive data among connected entities. Federated Learning (FL) has emerged as a privacy-preserving paradigm for collaborative model training without exposing raw data. However, a fundamental trade-off arises between the level of differential privacy (DP) protection and the number of communication rounds required. To address this, we propose the Unified State-Space Feedback Control Framework (USFCF), which introduces a dynamic feedback regulation mechanism that adaptively balances privacy protection and communication overhead. Roadside units (RSUs) serve as distributed coordinators, monitoring the evolving system state and adjusting the model aggregation frequency accordingly. When the system detects excessive privacy noise, it suppresses redundant communication; conversely, if privacy weakens, it increases update rounds to reinforce protection. The derivation of an optimization-driven update policy is constructed to realize the privacy-communication of the FL-DP system. Experimental evaluations on diverse datasets demonstrate that our method enhances privacy adaptability, reduces communication cost, and ensures robust performance for privacy-sensitive vehicular intelligence.