This chapter investigates the socially aware proactive edge caching strategy in vehicular social networks, where vehicles can be selected as caching nodes to assist content delivery. The objective is to achieve a trade-off between the cost of providing caching services and the content transmission latency. This strategy presents two challenges: (1) which vehicles can be selected as caching nodes, and (2) how to place content on these selected vehicles without violating user privacy. To address these issues, a novel community detection and attention-weighted federated learning-based proactive edge caching (CAFLPC) strategy is proposed. In this strategy, we first group vehicles into different communities on the basis of both the mobility and social properties of the vehicles and then select important vehicles (IVs) as caching nodes for each community by considering the social importance of the vehicles. To determine how to place the popular content in these selected IVs, an attention-weighted federated learning (AWFL)-based content popularity prediction framework is proposed. It integrates attention-weighted federated learning with a bidirectional long short-term memory network (AWFL_BiLSTM) to achieve higher content popularity prediction accuracy while protecting user privacy. Considering imbalances in the active levels and local computing capacities of the vehicles, an attention-weighted aggregation mechanism is proposed to improve training efficiency and prediction accuracy. The simulation results show that the proposed CAFLPC strategy outperforms existing caching strategies by approximately 2.2~35.1% in terms of transmission latency, which is reduced per unit cost.