Against the backdrop of rapidly advancing cloud storage technology, as well as 5G and 6G communication technologies, group key management faces increasingly daunting challenges. Traditional key management encounters difficulties in key distribution, security threats, management complexity, and issues of trustworthiness. Particularly in scenarios with a large number of members or frequent member turnover within groups, this may lead to security vulnerabilities such as permission confusion, exacerbating the security risks and management complexity faced by the system. To address these issues, this paper utilizes blockchain technology to achieve distributed storage and management of group keys. This solution combines key management with the distributed characteristics of blockchain, enhancing scalability, and enabling tracking of malicious members. Simultaneously, by integrating intelligent authentication mechanisms and lightweight data update mechanisms, it effectively enhances the security, trustworthiness, and scalability of the key management system. This provides important technical support for constructing a more secure and reliable network environment.

As the global digitalization process continues, information is transformed into data and widely used, while the data are also at risk of serious privacy breaches. The Cuckoo filter is a data structure based on the Cuckoo hash. It encrypts data when it is used and can achieve privacy protection to a certain extent. The Cuckoo filter is an alternative to the Bloom filter, with advantages such as support for deleting elements and efficient space utilization. Cuckoo filters are widely used and developed in the fields of network engineering, storage systems, databases, file systems, distributed systems, etc., because they are often used to solve collection element query problems. In recent years, many variants of the Cuckoo filter have emerged based on ideas such as improving the structure and introducing new technologies in order to accommodate a variety of different scenarios, as well as a huge collection. With the development of the times, the improvement of the structure and operation logic of the Cuckoo filter itself has become an important direction for the research of aggregate element query.

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.