AeroRec: An Efficient On-Device Recommendation Framework using Federated Self-Supervised Knowledge Distillation

Modern recommendation systems operate entirely on the basis of central servers, requiring users to upload their behavior data from mobile devices to these servers. This practice has raised concerns about data privacy among users. Federated learning (FL), a machine learning technique designed to protect privacy, is becoming the standard solution. By combining federated learning with recommendation systems, Federated Recommendation Systems (FRS) allow users to collaboratively train a shared recommendation model without transmitting their original behavior data. However, existing federated learning solutions disregard the inherent limitations of resource-constrained mobile devices, which include limited storage space, computational overhead, and communication bandwidth. Although deploying a lightweight recommendation model can address the constraints of mobile devices, achieving satisfactory accuracy is difficult even with substantial communication overhead incurred during multiple rounds of federated learning training due to the limitations of the lightweight model’s capabilities and the sparsity of recommendation data. To address this issue, we propose AeroRec, a n e fficient on-device Feder ated Recommendation Framework. In AeroRec, we use federated self-supervised distillation to enhance the global model after each parameter aggregation in each round. This approach not only accelerates the convergence rate but also surpasses the upper limit of the lightweight model’s capability, thereby enabling a higher accuracy. We demonstrate that AeroRec outperforms several state-of-the-art FRS frameworks regarding recommendation accuracy and convergence speed through extensive experiments on three real-world datasets.