RelayRec: Empowering Privacy-Preserving CTR Prediction via Cloud-Device Relay Learning

Click-through rate (CTR) prediction holds paramount importance across numerous applications, profoundly impacting user experience and business profitability. The freshness of a CTR prediction model significantly influences its performance, since users’ needs and interests may be changing over time, thereby requiring the model to be updated frequently. However, stringent data protection regulations have constrained the collection of users’ personal data, posing challenges to traditional model refreshing strategies that rely on centralized data collection. On-device learning techniques, such as federated learning (FL), offer a viable solution by enabling model training on devices without compromising user privacy. Nevertheless, the scarcity of training data with diverse distributions among devices presents considerable obstacles to on-device learning effectiveness. To address these challenges, we introduce RelayRec, a cloud-device relay learning framework designed for privacy-preserving CTR prediction. To establish competent initial models for devices, RelayRec categorizes pre-regulation cloud data into user preference groups, training preference-specific models for devices. Furthermore, a cloud-based automated model selector is developed to identify suitable initial models for devices. To elevate the relay learning performance of these initial models, we incorporate a personalized collaborative learning mechanism that aggregates device models based on user preferences. Extensive experimental evaluations underscore RelayRec’s superior performance compared to state-of-the-art benchmarks, affirming its efficacy in privacy-preserving CTR prediction.