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[2602.12567] Fractional Order Federated Learning for Battery Electric Vehicle Energy Consumption Modeling
Summary
This article presents a novel approach to federated learning for Battery Electric Vehicles (BEVs) using Fractional-Order Roughness-Informed Federated Averaging (FO-RI-FedAvg), which enhances model stability and accuracy under varying client participation conditions.
Why It Matters
The research addresses critical challenges in federated learning for BEVs, such as instability due to client variability and connectivity issues. By introducing FO-RI-FedAvg, the authors provide a solution that could lead to more reliable energy consumption modeling, which is essential for improving the efficiency and performance of electric vehicles.
Key Takeaways
- FO-RI-FedAvg improves stability and accuracy in federated learning for BEVs.
- The method incorporates adaptive mechanisms for better local optimization.
- Experimental results show enhanced performance with reduced client participation.
Computer Science > Machine Learning arXiv:2602.12567 (cs) [Submitted on 13 Feb 2026] Title:Fractional Order Federated Learning for Battery Electric Vehicle Energy Consumption Modeling Authors:Mohammad Partohaghighi, Roummel Marcia, Bruce J. West, YangQuan Chen View a PDF of the paper titled Fractional Order Federated Learning for Battery Electric Vehicle Energy Consumption Modeling, by Mohammad Partohaghighi and 3 other authors View PDF HTML (experimental) Abstract:Federated learning on connected electric vehicles (BEVs) faces severe instability due to intermittent connectivity, time-varying client participation, and pronounced client-to-client variation induced by diverse operating conditions. Conventional FedAvg and many advanced methods can suffer from excessive drift and degraded convergence under these realistic constraints. This work introduces Fractional-Order Roughness-Informed Federated Averaging (FO-RI-FedAvg), a lightweight and modular extension of FedAvg that improves stability through two complementary client-side mechanisms: (i) adaptive roughness-informed proximal regularization, which dynamically tunes the pull toward the global model based on local loss-landscape roughness, and (ii) non-integer-order local optimization, which incorporates short-term memory to smooth conflicting update directions. The approach preserves standard FedAvg server aggregation, adds only element-wise operations with amortizable overhead, and allows independent toggling of each co...
