PRISM: Reinforced Semantic Communication for Decentralized Federated Anomaly Detection in Vehicular Networks

Abstract

Decentralized Federated Learning (DFL) enables collaborative learning that preserves privacy, but faces critical communication challenges in bandwidth-constrained environments such as vehicular networks. We propose Policy-driven Representation and Information Selection Method (PRISM), a Reinforcement Learning (RL)-based method that optimizes semantic communication in DFL through adaptive representation selection and intelligent topology management. Our dual-model architecture separates task learning (autoencoder-based anomaly detection) from communication control (Proximal Policy Optimization (PPO)-based decision making), enabling agents to dynamically select between transmitting full model weights or compressed trajectory embeddings. We formulate this as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) and train using a Centralized Training Decentralized Execution (CTDE) paradigm. We demonstrate our method through a digital twin monitoring scenario where vehicles collaboratively detect trajectory anomalies in neighbors' behavior in real time, and the logged detections support forensic analysis, insurance claims, and fleet auditing. Experiments on Simulation of Urban Mobility (SUMO) generated Barcelona traffic data show an F1-score of 0.90, scaling from 2 to 128 agents with graceful degradation past N = 32, Byzantine-robust aggregation, and validation on Next Generation Simulation (NGSIM) US-101 real traces (F1 0.82). The learned policy exhibits emergent temporal behavior: transmitting weights early during training when model divergence is high, then shifting to embeddings as models converge, with the weight-to-embedding mix derived as the equilibrium of the communication budget constraint. The method generalizes to other bandwidth-limited collaborative learning domains.