Due to the increased adoption of edge computing, and growing concern about data privacy, Federated Learning (FL) frameworks have gained significant attention as a promising approach to leverage distributed data while protecting user privacy. However, ensuring the privacy of model updates from multiple edge devices is a significant challenge due to the potential privacy breaches and security vulnerabilities. In this paper, we propose EdgeSA a novel approach that leverages secure aggregation for privacy-preserving federated learning in edge computing. EdgeSA ensures that local model updates are gathered from edge devices in a way that respects privacy and integrity. Our comprehensive security analysis and experiments, demonstrate the effectiveness and efficiency of EdgeSA. The implementation results show that our scheme reduces the computational overhead on edge devices while achieving the same training accuracy as traditional federated learning schemes.