The project developed by Amanda Lopes (UFPA), John Sousa (UFPA), Lucas de Lima Bastos (UNIFESSPA), Lucas de Sousa Pacheco (UFPA), Iago Medeiros (UFPA), Denis Rosário (UFPA), and Eduardo Coelho Cerqueira (UFPA) was presented at SBRC 2026.
Vehicles are increasingly capable of collecting large amounts of data through sensors and IoT devices. In the context of autonomous driving, one approach is to equip cars with multiple sensors and sufficient computing power to process this information and estimate, in real time, the best route to take.
With this processing, the vehicle itself can make decisions, such as braking, accelerating, or changing its course. However, the potential is even greater when different vehicles can participate in the same network and share information. Based on the data and predictions generated by these vehicles, it is possible, for example, to identify the best routes and anticipate traffic situations.
It is in this context that connected autonomous vehicles have emerged: an attempt to expand the capabilities of vehicle systems to solve problems that the hardware available in a single vehicle cannot solve on its own. The idea is to leverage the knowledge generated by academia and create solutions that can, in the future, be implemented by the industry.
One of the challenges is enabling the vehicle to “read” its surrounding context and use that information to make more appropriate decisions. This is where PRINCE comes in—a federated learning (FL) architecture designed for autonomous vehicle networks.
The proposal aims to select, from among the available vehicles, those best suited to participate in a federated learning process. Instead of considering all vehicles in the network indiscriminately, the system evaluates different characteristics to determine which ones can contribute most significantly to the learning process.
Proactive customer selection, as exemplified by PRINCE, offers significant advantages over traditional reactive methods in Connected and Autonomous Vehicles (CAVs) environments, particularly in terms of resource efficiency, convergence speed, and model stability. While reactive approaches select vehicles and address any failures only after they occur, the proactive strategy seeks to anticipate potential problems and select, from the outset, the clients most likely to complete training.
One of the main benefits lies in the efficient use of resources. In reactive methods, bandwidth and processing capacity can be wasted when selected vehicles drop out or fail during a training round. The proactive approach seeks to pre-filter unstable nodes, achieving—according to the presented results—an improvement of up to 3.5 times in resource efficiency. This proactive approach also contributes to a significant reduction in connection failures—up to 2.2 to 2.5 times fewer compared to reactive approaches—thanks to the use of stochastic models to predict instabilities and pre-filtering based on signals such as RSSI.
These gains are also reflected in the model’s performance. PRINCE achieved a final accuracy of 83.90%, outperforming state-of-the-art reactive models by up to 9.22%. Furthermore, proactive selection accelerates convergence: in one of the evaluated scenarios, the system reached 60% accuracy three times faster than random selection methods, while also outperforming reactive approaches such as ACSP-FL and ECS-HDSR.
Another key advantage lies in reduced latency and computational costs. In reactive strategies, such as ECS-HDSR, when a client fails, the system must find a replacement and restart or reorganize part of the process, which adds steps, time, and processing load. The proactive approach seeks to avoid this situation by pre-selecting a set of clients with a high probability of remaining connected and completing the training.
This proactive approach also promotes training stability. While reactive methods may exhibit fluctuations in accuracy and spikes in the loss function due to the inclusion of models from failed clients, proactive selection tends to produce a more stable and consistent learning trajectory.
In summary, the fundamental difference lies in how each strategy handles uncertainty. Reactive selection waits for a problem to occur before attempting to correct it; proactive selection uses vehicle behavior history, network conditions, and mobility forecasts to anticipate potential failures and prevent them. In a dynamic environment such as that of connected and autonomous vehicles, this predictive capability can make each communication cycle more efficient, reduce waste, and contribute to global models that converge more quickly and stably.
Factors Considered in the Active Selection of Vehicles
The first factor considered is the location and connectivity of the vehicles, which are determined by the Received Signal Strength Indicator (RSSI). Vehicles that are closer or farther away—and whether or not they have internet coverage—are evaluated. Vehicles with weak RSSI values (i.e., values below -85 dbM) are excluded from further selection, as they do not have sufficient connectivity in that area to actively participate in the federated learning client selection process. Vehicles with good RSSI values (i.e., values above -85 dbM) are selected for the next stage.
To estimate the likelihood that a vehicle will remain connected during the process, in the second step the architecture uses the concept of a Markov chain—a stochastic process in which the system can be in a given state and either remain in that state or transition to another. Based on this model, the probability that a given vehicle will remain connected to the network is calculated.
This information is important because, in a federated learning process, it is not enough for a vehicle to have relevant data: it must also be able to remain connected for the time necessary to participate in the training.
The Importance of Data Heterogeneity
The third and final criterion used by PRINCE is the diversity of the data available in each vehicle. To this end, the architecture takes into account Shannon entropy, a measure related to the variability and diversity of information.
The reasoning is that vehicles with more diverse data can make a more meaningful contribution to the learning process, since they allow the model to encounter different situations and patterns.
Thus, the system seeks to prioritize vehicles with a higher entropy index—that is, greater diversity in their data. The architecture therefore combines different criteria to select the most suitable vehicles to participate in federated learning. Generally speaking, three dimensions are considered: proximity—prioritizing vehicles that are close to the region or situation of interest; connectivity—selecting vehicles most likely to remain connected during the process; and data heterogeneity—prioritizing vehicles whose data exhibit greater diversity.
The goal is to find a set of vehicles that is not only related to the problem we want to analyze but is also capable of effectively contributing to the model’s training.
Possible Applications
One possible application would be the use of PRINCE by a company that needs to transmit specific information to certain vehicles in a given region.
Imagine, for example, that a problem is occurring on a particular route. The company could use the architecture to identify vehicles that are in that area or heading toward it and share relevant information with those users.
Similarly, a driver traveling to a specific city could receive specific information related to conditions in that region, allowing the system to use the data generated by connected vehicles to support more informed decisions.
Read the full article in the SBRC 2026 proceedings