Vector search enhances federated learning security in autonomous vehicles by enabling efficient, privacy-preserving validation of model updates without exposing raw data. Federated learning allows vehicles to collaboratively train machine learning models by sharing parameter updates instead of raw sensor data, which helps protect user privacy. However, this approach still faces risks like malicious actors submitting manipulated updates to degrade the global model. Vector search addresses these risks by comparing the mathematical “fingerprints” of updates against trusted patterns, ensuring only valid contributions are aggregated.
For example, autonomous vehicles generate model updates based on local driving data (e.g., camera, lidar, and radar inputs). Before these updates are sent to the central server, vector search can analyze their embedding vectors—numerical representations of the learned features—to detect anomalies. A server could maintain a database of known safe update patterns and use approximate nearest neighbor (ANN) algorithms to check if incoming vectors align with expected behavior. If an update’s vector deviates significantly (e.g., due to adversarial tampering), it can be flagged for review or discarded. This process works without decrypting the actual model parameters, maintaining data confidentiality. Tools like FAISS or HNSW libraries enable efficient comparisons even with high-dimensional vectors, making this feasible for real-time systems.
Additionally, vector search helps prevent model poisoning attacks by identifying inconsistencies in update distributions. Autonomous vehicles operating in similar environments (e.g., urban vs. highway) should produce updates with comparable vector characteristics. If a subset of vehicles suddenly submits vectors clustering far from the majority, this could indicate compromised devices or spoofed data. For instance, a malicious actor trying to inject false pedestrian-detection patterns would create vectors that don’t align with the broader dataset. By applying distance metrics like cosine similarity during aggregation, federated learning systems can automatically downweight or exclude outliers. This approach complements cryptographic techniques like homomorphic encryption, adding a layer of semantic validation to ensure updates are not just securely transmitted but also logically consistent with the learning task.