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 within vehicular networks offers an efficient mechanism for model training while preserving raw sensory data integrity. Yet%2C FL struggles with non-identically distributed data (e.g.%2C quantity skew)%2C leading to suboptimal convergence rates during model training. In previous work%2C we introduced FedLA%2C an innovative Label-Aware aggregation method addressing data heterogeneity in FL for generic scenarios.%0A In this paper%2C we introduce FedProx%2BLA%2C a novel FL method building upon the state-of-the-art FedProx and FedLA to tackle data heterogeneity%2C which is specifically tailored for vehicular networks. We evaluate the efficacy of FedProx%2BLA in continuous online object detection model training. Through a comparative analysis against conventional and state-of-the-art methods%2C our findings reveal the superior convergence rate of FedProx%2BLA. Notably%2C if the label distribution is very heterogeneous%2C our FedProx%2BLA approach shows substantial improvements in detection performance compared to baseline methods%2C also outperforming our previous FedLA approach. Moreover%2C both FedLA and FedProx%2BLA increase convergence speed by 30%25 compared to baseline methods.?quality=80&w=800)
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