Fractional Order Federated Learning for Battery Electric Vehicle Energy Consumption Modeling
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Abstract (arXiv)
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 component. Experiments on two real-world BEV energy prediction datasets, VED and its extended version eVED, show that FO-RI-FedAvg achieves improved accuracy and more stable convergence compared to strong federated baselines, particularly under reduced client participation.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 53 references that are in the map. First posted on arXiv 13 Feb 2026.
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References · 27
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2019Advances and Open Problems in Federated Learningcited by 3,021
- 2018Federated Optimization in Heterogeneous Networkscited by 2,971
- 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
- 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
- 2020Sharpness-Aware Minimization for Efficiently Improving Generalizationcited by 1,404
- 2016On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minimacited by 1,337
- 2017Visualizing the Loss Landscape of Neural Netscited by 1,126
- 2018Federated Learning with Non-IID Datacited by 1,099
- 2020Adaptive Federated Optimizationcited by 1,036
- 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
- 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
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- 2019Towards Federated Learning at Scale: System Designcited by 826
- 2018Local SGD Converges Fast and Communicates Littlecited by 595
- 2018Federated Learning for Mobile Keyboard Predictioncited by 565
- 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
- 2021Federated Learning Based on Dynamic Regularizationcited by 555
- 2017Sharp Minima Can Generalize For Deep Netscited by 462
- 2019An Investigation into Neural Net Optimization via Hessian Eigenvalue Densitycited by 289
- 2019Tighter Theory for Local SGD on Identical and Heterogeneous Datacited by 266
- 2018Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learningcited by 211
- 2019On the Convergence of Local Descent Methods in Federated Learningcited by 150
- 2022Towards Understanding Sharpness-Aware Minimizationcited by 145
- 2023Stochastic Controlled Averaging for Federated Learning with Communication Compressioncited by 100
- 2020Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learningcited by 78
- 2022Extended vehicle energy dataset (eVED): an enhanced large-scale dataset for deep learning on vehicle trip energy consumptioncited by 4
- 2025FedEL: Federated Elastic Learning for Heterogeneous Devicescited by 3
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