Accuracy-Improved Privacy-Preserving Asynchronous Federated Learning in IoT

To address the asynchronous challenges stemming from user resource constraints and intermittent network connections in distributed Internet of Things (IoT) systems, asynchronous federated learning (AFL) has been extensively explored in both academic and industrial domains. However, existing AFL approaches still struggle to effectively tackle the issue of model aggregation information loss due to delayed users, which leads to degraded model accuracy. To alleviate the impact of delayed or unavailable model updates on model aggregation, we propose a novel model update enhancement method to compensate for the loss of model aggregation information. Specifically, we utilize delayed model updates as an update agent for this user and correct the weights of these updates within the delayed rounds threshold to ensure the delayed user’s contribution to the model aggregation. Additionally, we integrate symmetric homomorphic encryption (SHE) into AFL to ensure user privacy while simultaneously minimizing computational overhead. Lastly, we conduct extensive experiments to demonstrate that our scheme improves model accuracy by 6.53% compared to state-of-the-art solutions. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/MiaoGroup-XDU/acc-improved-ppafl</uri>.