Differentially Private Asynchronous Federated Learning with Buffered Aggregation for Face Recognition

The rise of privacy concerns in face recognition applications necessitates the development of secure machine learning frameworks. This paper introduces Differentially Private Asynchronous Federated Learning with Buffered Aggregation (DP-AFLBA), a novel approach that combines differential privacy, asynchronous federated learning, and buffered aggregation to protect sensitive data while improving model accuracy. In DP-AFLBA, local model updates from participants are perturbed with carefully calibrated noise to ensure privacy, adhering to (ϵ, δ)-differential privacy standards. Asynchronous communication allows participants to send updates independently, reducing delays associated with traditional synchronous methods. To address the challenges of stale updates, a buffering mechanism aggregates updates over a defined time window, enabling the server to apply a more stable and accurate global model update. Experimental results demonstrate that DP-AFLBA achieves superior performance in terms of model accuracy and privacy protection compared to existing federated learning frameworks, making it particularly suitable for large-scale face recognition tasks.