AdaFed: Adaptive Selective Aggregation for Heterogeneous Federated Learning in Autonomous Driving

Abstract

We present a comprehensive empirical study of federated learning (FL) for seven heterogeneous autonomous driving models and propose AdaFed, an adaptive selective aggregation strategy. We introduce a tier-based partial backbone sharing framework that groups models by shared architectural components (ResNet-34/50 backbones, bird’s-eye-view (BEV) encoders, and BEV-Lift modules) and evaluate six FL strategies: FedAvg, FedProx, SCAFFOLD, FedMD, FedDF, and AdaFed. Across all seven models, no FL strategy surpasses the per-model imitation-learning (IL) baseline; the practical question is therefore which strategy minimises degradation. AdaFed applies a tier-specific base strategy, blends the aggregated and local weights with an adaptive coefficient, and reverts any round-level update that worsens validation error. Experiments on CARLA 0.9.16 reveal that (1) partial backbone averaging stays closest to the IL baseline for ResNet-50 models (+0.05–0.13 m average displacement error (ADE)), (2) knowledge-distillation (KD) based approaches degrade performance, and (3) AdaFed yields the best FL result for the two models most exposed to negative transfer—VAD (2.05 m, −0.26 m vs. the best conventional FL strategy) and UniAD (1.52 m). These findings provide practical guidelines for heterogeneous FL deployment in autonomous driving.