Federated Learning (FL) represents the de facto approach for distributed training of machine learning models. Nevertheless, researchers have identified several security and privacy FL issues. Among these, the lack of anonymity exposes FL to linkability attacks, representing a risk for model alteration and worker impersonation, where adversaries can explicitly select the attack target, knowing its identity. Named-Data Networking (NDN) is a novel networking paradigm that decouples the data from its location, anonymising the users. NDN embodies a suitable solution to ensure workers’ privacy in FL, thus fixing the abovementioned issues. However, several issues must be addressed to fit FL logic in NDN semantics, such as missing push-based communication in NDN and anonymous NDN naming convention. To this end, this paper contributes a novel anonymous-by-design FL framework with a customised communication protocol leveraging NDN. The proposed communication scheme encompasses an ad-hoc FL-oriented naming convention and anonymity-driven forwarding and enrollment procedures. The anonymity and privacy requirements considered during the framework definition are fully satisfied through a detailed analysis of the framework's robustness. Moreover, we compare the proposed mechanism and state-of-the-art anonymity solutions, focusing on the communication efficiency perspective. The simulation results show latency and training time improvements up to ∼30%, especially when dealing with large models, numerous federations, and complex networks.

Assisted and autonomous driving are rapidly gaining momentum and will soon become a reality. Artificial intelligence and machine learning are regarded as key enablers thanks to the massive amount of data that smart vehicles will collect from onboard sensors. Federated learning is one of the most promising techniques for training global machine learning models while preserving data privacy of vehicles and optimizing communications resource usage. In this article, we propose vehicular radio environment map federated learning (VREM-FL), a computation-scheduling co-design for vehicular federated learning that combines mobility of vehicles with 5G radio environment maps. VREM-FL jointly optimizes learning performance of the global model and wisely allocates communication and computation resources. This is achieved by orchestrating local computations at the vehicles in conjunction with transmission of their local models in an adaptive and predictive fashion, by exploiting radio channel maps. The proposed algorithm can be tuned to trade training time for radio resource usage. Experimental results demonstrate that VREM-FL outperforms literature benchmarks for both a linear regression model (learning time reduced by 28%) and a deep neural network for semantic image segmentation (doubling the number of model updates within the same time window).