Intelligent Driving: How Federated Machine Learning is changing the automotive industry - SiDi

They say that the automobile is the computer on wheels due to the growing presence of technology and advanced computer systems embedded in modern vehicles. As the automotive industry has evolved, cars have become increasingly complex and sophisticated, incorporating a variety of electronic features that have a wide range of functionalities, integration with other devices, connectivity for the exchange of information, among many other applications .

According to McKinsey, "The automotive sector is set to be the fastest growing segment between 2022 and 2030." And there are four major trends that should be consolidated in Brazil by then: shared vehicles, autonomous, connected and electric cars. In the case of shared mobility, Brazil is one of the countries adopting this option the most .

As vehicles become increasingly connected and intelligent, the demands for innovative solutions increase and the need to process large volumes of data efficiently and preserve privacy becomes crucial. Federated learning then emerges as a promising approach to drive the evolution of the industry, offering the ability to train decentralized machine learning models and create diverse applications using data distributed across devices and servers. This innovative approach has the potential to propel the automotive industry to new heights of efficiency, convenience and safety .

We analyzed several disruptive applications that could have a major impact on the industry, such as:

• Vehicle safety improvements: anomaly detection and accident prevention models in real time, using sensor data collected from multiple vehicles. This would make it possible to improve vehicle safety by detecting and correcting problems quickly;

• User experience improvements: training personalization models to improve the user experience in connected vehicles. Usage data and user preferences can be combined to create a personalized model that meets the user's needs;

• Fuel efficiency optimization: training fuel optimization models in real time, using driving data collected from various vehicles. This would enable improvements in fuel efficiency and a reduction in greenhouse gas emissions;

• Improving problem diagnosis: training problem diagnosis models in real time, using sensor data collected from various vehicles. This would make it possible to quickly identify and correct problems and prevent damage.

These are examples of solutions that could revolutionize the automotive sector, bringing with them a host of benefits and opportunities. The decentralization of model training allows vehicles to become increasingly intelligent, adapting to users' individual preferences and needs. With the rapid evolution of Federated Learning, we can look forward to a new era of connected and autonomous vehicles, transforming the way we get around, making driving safer, more efficient and more enjoyable for everyone involved.