In the Internet of Vehicles (IoV), Federated Learning (FL) provides a privacy-preserving solution by aggregating local models without sharing data. Traditional supervised learning requires image data with labels, but data labeling involves significant manual effort. Federated Self-Supervised Learning (FSSL) utilizes Self-Supervised Learning (SSL) for local training in FL, eliminating the need for labels while protecting privacy. Compared to other SSL methods, Momentum Contrast (MoCo) reduces the demand for computing resources and storage space by creating a dictionary. However, using MoCo in FSSL requires uploading the local dictionary from vehicles to Base Station (BS), which poses a risk of privacy leakage. Simplified Contrast (SimCo) addresses the privacy leakage issue in MoCo-based FSSL by using dual temperature instead of a dictionary to control sample distribution. Additionally, considering the negative impact of motion blur on model aggregation, and based on SimCo, we propose a motion blur-resistant FSSL method, referred to as BFSSL. Furthermore, we address energy consumption and delay in the BFSSL process by proposing a Deep Reinforcement Learning (DRL)-based resource allocation scheme, called DRL-BFSSL. In this scheme, BS allocates the Central Processing Unit (CPU) frequency and transmission power of vehicles to minimize energy consumption and latency, while aggregating received models based on the motion blur level. Simulation results validate the effectiveness of our proposed aggregation and resource allocation methods.

翻译：在车联网中，联邦学习通过聚合本地模型而不共享数据，提供了一种隐私保护的解决方案。传统的监督学习需要带标签的图像数据，但数据标注涉及大量人工工作。联邦自监督学习利用自监督学习在联邦学习中进行本地训练，在保护隐私的同时消除了对标签的需求。与其他自监督学习方法相比，动量对比通过创建字典降低了对计算资源和存储空间的需求。然而，在联邦自监督学习中使用动量对比需要将本地字典从车辆上传到基站，这带来了隐私泄露的风险。简化对比通过使用双温度而非字典来控制样本分布，解决了基于动量对比的联邦自监督学习中的隐私泄露问题。此外，考虑到运动模糊对模型聚合的负面影响，我们在简化对比的基础上提出了一种抗运动模糊的联邦自监督学习方法，简称BFSSL。进一步地，我们通过提出一种基于深度强化学习的资源分配方案（称为DRL-BFSSL）来解决BFSSL过程中的能耗和延迟问题。在该方案中，基站根据运动模糊程度聚合接收到的模型，同时分配车辆的中央处理器频率和传输功率以最小化能耗和延迟。仿真结果验证了我们所提出的聚合与资源分配方法的有效性。