The optimal RRT in elliptic space sampling (Informed-RRT*) is an extension of RRT that provides asymptotic optimality, however, it experiences gradual progress and close to obstacles. In the paper, we propose a novel path planning algorithm guided bidirectional Informed-RRT* (BI-RRT*), that introduces extension range, dual-direction exploration, and refinement in trajectory design. The growth range refers to maintaining an additional area from the obstacle to enhance the dependability of the path through preventing impacts. Bidirectional search is a search strategy using both start and target points for a initial solution. Smoothing improves path robustness by using cubic spline. Furthermore, simulation tests for the BI-RRT* algorithm are executed, and the efficacy of the suggested algorithm is confirmed through its application in a robot operating system (ROS). Simulations and experimental tests verify that the proposed algorithm improves the path planning capability. We emphasize the importance of safety, privacy, and reliability in the deployment of AI systems. Our algorithm ensures that the planned paths maintain a safe distance from obstacles, reducing the risk of collisions. Additionally, we prioritize privacy by adhering to data protection regulations and implementing secure communication protocols within the AI system. Moreover, we have applied rigorous testing and validation processes to enhance the reliability of our algorithm, ensuring consistent and accurate path planning outcomes.

Edge drones continuously collect sensitive information such as telemetry data during missions, making it difficult to apply centralized model training directly due to privacy protection, security compliance, and regulatory constraints. Although federated learning (FL) can avoid sharing raw data, existing federated learning schemes based solely on homomorphic encryption (HE) still face security risks in drone scenarios, such as gradient inversion, member inference, and malicious update injection. To address this, we propose a secure and verifiable edge federated learning framework for parameter-efficient model adaptation in drone scenarios. The framework introduces homomorphic encryption for model updates on the device side to protect the privacy of updates before transmission and aggregation. Simultaneously, on the server side, decryption, aggregation, and verification are performed through a remotely authenticated Trusted Execution Environment (TEE), thereby limiting the server's access to plaintext updates and reducing the feasibility of gradient inversion and member inference attacks at the system level. Furthermore, an aggregation signature mechanism is introduced to batch verify the identity and update integrity of participating nodes, effectively preventing malicious or tampered updates from participating in aggregation, thus overcoming the shortcomings of existing HE-FL schemes in terms of poisoning resistance and verifiability. Experimental results show that, while ensuring safety and verifiability, the proposed method improves model accuracy by 3% compared to the comparative scheme, while maintaining better performance in terms of computation and communication overhead, thus verifying the practicality and deployability of the framework in resource-constrained UAV edge environments.

To address the security challenges of untrusted servers and the difficulty in verifying malicious node identities in UAV swarm federated learning when relying solely on homomorphic encryption (HE), this paper proposes a verifiable HE-based federated learning (VHEFL) framework that integrates trusted execution environments (TEEs) with aggregate signatures. The method proceeds through a series of well-defined steps. Initially, each UAV computes model gradients locally, encrypts them using additive HE, and attaches a lightweight identity-based digital signature. The encrypted model gradients and signatures are uploaded to the server. Subsequently, the ground server performs gradient aggregation, including both the encrypted gradients and signatures, in the regular execution environment (REE). After performing aggregation in the REE, the aggregated ciphertext and all the signatures are sent to the TEE. Inside the TEE, an efficient aggregate verification algorithm is executed to batch-verify the authenticity and integrity of all signatures simultaneously. This enables the TEE to securely verify the aggregated result and return the aggregated model parameters to the UAVs, where the encrypted model parameters are decrypted locally to obtain the updated parameters. By combining low-overhead aggregate signature verification with hardware isolation, VHEFL provides a high-performance and verifiable security solution that effectively addresses the challenges of data privacy, malicious update prevention, and computational overhead in UAV swarm federated learning.