In Vehicular Ad-hoc Networks (VANETs), federated learning (FL) enables collaborative training a global intelligent transportation model without sharing vehicles’ raw data. Achieving model convergence in VANETs requires multiple rounds of FL for aggregation and updates. To improve the efficiency of model convergence, researchers explore methods of multi-round aggregation. The latest work, Flamingo (S&P 2023), proposes a multi-round aggregation scheme based on a reusable key mechanism. Its application in VANETs with dynamic network structures enhances the efficiency of model training. However, this scheme has a defect: vehicles cannot verify the correctness of the aggregation result. Once a semi-trusted server returns incorrect aggregation results due to computational errors, it may reduce the model’s accuracy or even cause training failure. In this scheme, we propose a verifiable multi-round aggregation scheme for FL in VANETs (VMFL), enabling vehicles to verify the aggregation results. Firstly, a multi-round verification mechanism is designed to reduce the number of interactions for vehicles by using reusable keys. Additionally, we propose a lightweight proof scheme that allows vehicles to verify the results with minimal computation, reducing the computational overhead of the verification process. Finally, a security analysis of VMFL is performed to demonstrate its security in cases of vehicle dropout. We validated the efficiency of VMFL across different datasets through experiments, showing no significant increase in time overhead compared to Flamingo, and demonstrating that its verification time is reduced by about 90% compared to the state-of-the-art scheme, thereby demonstrating the scheme’s usability.

Blockchain database has been widely applied in various fields, providing a secure architecture for data storage and sharing. In blockchain databases, the use of sharding technology can enhance the system’s scalability and improve transactions in the network to be processed in parallel. However, when applying searchable encryption techniques to query encrypted data in sharding blockchains, frequent data access to different shards results in uneven load distribution, leading to hotspot issues and reducing system efficiency. To tackle the challenge, we presentSDB, a scheme integrating searchable encryption with sharding technology to enhance the scalability of blockchain databases. Firstly, we introduce a two-stage sharding mechanism. It performs pre-sharding based on keywords, and then implements fine-grained dynamic adjustments through improved jump consistent hashing, effectively resolving the load imbalance problem. Secondly, we present a cross-shard query strategy based on encrypted indexes, which constructs verifiable indexes for encrypted data and converts user queries into a multiway query tree. This addresses the cross-shard query optimization problem in encrypted environments. Under the experiment and security analysis,SDBachieves efficient cross-shard queries with higher query performance and throughput, at least 30% and 20% higher than state-of-the-art scalable blockchain database schemes.