In the current era of the Internet of Vehicles (IoV), vehicle to vehicle data sharing can provide customized applications for Connected and Autonomous Vehicles (CAVs). The advancement of Deep Learning (DL) methodologies is one of the key driving forces for CAVs, allowing elaborating a massive amount of data by the resource-constrained onboard devices. In a traditional centralized DL approach, vehicle data are transmitted to the cloud for the training of models. This approach leads to significant communication overhead, high delays, and data privacy concerns. Conversely, Federated Learning (FL) performs the training using the local models in a distributed fashion and mitigates the data privacy risks by sharing only the model parameters with the server, optimizing the FL to be used with resources-constrained devices. In this paper, we propose the design of a scalable communication infrastructure to support the FL procedure based on Information-Centric Networking (ICN) using Apache Kafka, called KafkaFed. The ICN-based infrastructure allows to overcome the shortcomings of current client-server architectures for FL, in which routing is content-based or name-based to achieve efficient data retrieval for mobile nodes. In ICN, data are stored at intermediate nodes to provide efficient and reliable data delivery. A proof of concept of the KafkaFed communication architecture is developed and tested in an emulated environment. The performance of the proposed framework compared to the client server-based FL architecture, i.e., FLOWER showed a boost of almost 40% with just 32 clients in addition to several other advantages of scalability, reliability, and security