针对车联网异构数据，提出动态选参机制提升联邦学习效率。
FedCLF -- Towards Efficient Participant Selection for Federated Learning in Heterogeneous IoV Networks
- 用校准损失作为选参依据，结合反馈控制调节客户端采样频率。
- 在高异构数据下模型准确率最高提升16%，采样频率降低。
- 适合资源受限的动态车联网场景，显著提升训练效率。
联邦学习（FL）通过仅共享训练参数而非原始数据，保护隐私，适用于高度动态、异构且对时间敏感的互联网车辆（IoV）网络。然而，高数据与设备异构性给FL带来挑战。为此，本文提出FedCLF（带校准损失与反馈控制的联邦学习），将校准损失引入参与者选择过程，并采用反馈控制机制动态调整客户端采样频率。该方法在高异构数据下显著提升整体模型精度，并优化资源受限的IoV网络中的资源利用率，从而提高FL效率。在使用CIFAR-10数据集、不同异构程度下的实验表明，相比基线方法FedAvg、Newt和Oort，FedCLF在高异构场景下准确率最高提升16%，同时采样频率更低，效率更优。
原文摘要 · Abstract (English)
Federated Learning (FL) is a distributed machine learning technique that preserves data privacy by sharing only the trained parameters instead of the client data. This makes FL ideal for highly dynamic, heterogeneous, and time-critical applications, in particular, the Internet of Vehicles (IoV) networks. However, FL encounters considerable challenges in such networks owing to the high data and device heterogeneity. To address these challenges, we propose FedCLF, i.e., FL with Calibrated Loss and Feedback control, which introduces calibrated loss as a utility in the participant selection process and a feedback control mechanism to dynamically adjust the sampling frequency of the clients. The envisaged approach (a) enhances the overall model accuracy in case of highly heterogeneous data and (b) optimizes the resource utilization for resource constrained IoV networks, thereby leading to increased efficiency in the FL process. We evaluated FedCLF vis-à-vis baseline models, i.e., FedAvg, Newt, and Oort, using CIFAR-10 dataset with varying data heterogeneity. Our results depict that FedCLF significantly outperforms the baseline models by up to a 16% improvement in high data heterogeneity-related scenarios with improved efficiency via reduced sampling frequency.
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