# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 5 Collaborative Learning Technologies
### Federated Learning for IoV  
[PERSON] et al. [97] discuss FL in detail from the perspectives of communication cost, resource allocation, security and privacy, and applications in mobile edge computing, and point out the challenges and feasible research directions. They believe that FL has strong optimization potential in mobile edge computing, and FL is an important scheme used to  
Figure 6: Learning parallelism (left: data parallelism, the same model; right: model parallelism, different models). realize collaborative machine learning by transforming the centralized cloud computing model into a distributed edge-cloud collaborative computing. [PERSON] et al. [98] study the routing protocol, framework, architecture, and hardware requirements of FL, then illustrate its feasibility through two application cases, and explain its advantages and challenges in edge computing from the perspective of engineering applications. The conclusion they come to is that FL is highly suitable for edge computing tasks because it utilizes the data and computing power of edges effectively. [PERSON] and [PERSON] [99] conduct a theoretical analysis about the latency and loss performance for multiple TCP connections in a Wi-Fi-available vehicular environment, and introduce a FL method to maximize system performances while maintaining the fairness among different TCP flows. Learning models are installed in vehicles, and the learning results are sent to a federal coordinator that is connected to vehicles through RSUs. [PERSON] et al. [100] discuss the delay-aware task offloading problem in vehicular edge computing environments, where different types of communication approaches--namely dedicated Short Range Communication (DSRC), cellular vehicle-to-everything (V2X) communication, and millimeter wave (mmWave) communication--are available. In order to efficiently utilize the available communication and computing resources to minimize the task execution delay, they propose a FL-based approach to learn the best allocation policy in complex vehicular environments. Q-learning is used at each FL client to find the local best policies, and the learned results from multiple FL clients are aggregated to deal with different conditions. They use computer simulations to show that the FL-based approach can achieve a higher offloading success rate and better utilization of the communication and computing resources compared to existing baselines. [PERSON] and [PERSON] [101] consider a aerial-terrestrial networking scenario where the UAVs serve as base stations. They first introduce a distributed heterogeneous computing platform (HCP) integrating UAVs and terrestrial base stations. Then, they propose a two-stage federated learning algorithm to enhance the content caching process. The first stage of local training is conducted at mobile devices, and the second stage of training is conducted at UAVs. HCP aggregates the local models from mobile devices and UAVs to generate the global model. [PERSON] et al. [102] discuss the use of federated learning to estimate the intelligence level of computer systems. They propose a vertical federated learning framework to conduct an intelligence ranking of autonomous vehicles without disclosing user data. Experimental results show that the FL-based approach outperforms existing baselines with acceptable time efficiency on both real and simulated data. [PERSON] et al. [103] discuss the joint communication, computing, and caching resource allocation problem for Ultradense edge computing environments, including smart vehicle systems. They design a DRL approach to minimize the task offloading delay and network resource consumption. A FL approach is also used to train the DRL model in a distributed manner for the purpose of data privacy protection. Experimental results demonstrate the effectiveness of both the DRL and FL. [PERSON] et al. [104] consider the control problem of a massive population of UAVs in communication-resource-limited environments. A FL approach is used to distribute the learning process.