Editing Federated Learning (section)

=== 5.7.3 Autonomous Vehicles ===

Autonomous vehicles (AVs), including self-driving cars and drones, generate enormous volumes of sensor data in real time—ranging from camera feeds and LiDAR scans to GPS coordinates and driver behavior logs. Federated Learning (FL) offers a compelling framework for training collaborative AI models across fleets of vehicles without the need to transmit this raw, privacy-sensitive data to the cloud. Instead, each vehicle trains a local model and only communicates encrypted or compressed model updates to a central or distributed aggregator<sup>[1][3]</sup>.

A major application of FL in AVs is in the area of *environmental perception* and *object detection*. Vehicles encounter different driving conditions across cities, weather patterns, and traffic densities. Using FL, vehicles can share what they learn about rare or localized road events—such as construction zones or unusual signage—without disclosing personally identifiable sensor data. This helps build more generalized and robust global models for autonomous navigation<sup>[2]</sup>. Companies like Tesla and Toyota Research Institute have explored federated or decentralized learning systems for AVs, aiming to continuously improve on-board models without compromising customer privacy.

FL is also leveraged for *driver behavior modeling* in semi-autonomous vehicles. By locally analyzing acceleration patterns, braking habits, and reaction times, vehicles can personalize safety recommendations or driving assistance features. When aggregated via FL, this data helps improve shared predictive models for accident avoidance or route optimization across entire fleets<sup>[4]</sup>.

However, the edge environments of AVs present unique challenges. Vehicles often operate with intermittent connectivity, variable compute resources, and high mobility. To address this, researchers have proposed *asynchronous FL protocols* and *hierarchical aggregation* where vehicles communicate with nearby edge nodes or road-side units (RSUs) that serve as intermediaries for model updates<sup>[5]</sup>. Additionally, ensuring security against *model poisoning attacks* is critical in vehicular networks. Robust aggregation and anomaly detection mechanisms are used to validate updates from potentially compromised vehicles<sup>[3][4]</sup>.

As AV ecosystems scale, FL is emerging as a key architecture for building secure, adaptive, and collaborative intelligence among connected vehicles—paving the way for safer roads and real-time cooperative decision-making.