GCPN: A Group Connected based Method for Continual Vertical Federated Recommender Systems in Data Ecosystems

Data ecosystems (DE) are the future directions of data management and play a vital role in unlocking the value of data. Service Recommender Systems (RS) are typical applications in DEs. For example, deep learning-based RS on the basis of extensive data from various fields can help organizations obtain valuable insights of data. As organizations from different fields share data for better recommendation services, the risk of privacy leakage which is harmful to DEs increases. Due to privacy concerns, Vertical Federated Learning (VFL), a privacy-preserving computing technology for joint learning and privacy recommendation models among organizations in various fields, has garnered significant attention. Existing VFL methods are training models with static data from specific fields when there are new recommendation scenarios or fields. In addition, the models are fixed after one training session. Therefore, these models can only be applied to several specific recommendation fields and they can't utilize continuously generated data that corresponds to various fields, posing challenges for long-term and extensive cooperation. To tackle these challenges, we introduce Vertical Federated Continual Learning (VFCL), which extends Continual Learning (CL) into the VFL framework to enable VFL models to sustainably adapt to new scenarios. We discuss feasible solutions based on existing CL methods. Furthermore, we propose GCPN, a method based on a dynamic architecture in VFCL. GCPN introduces fewer parameters for each new field by utilizing group connected layers and scale layers, eliminating the need for storing or using past data. It effectively alleviates the problem of catastrophic forgetting, a major issue in CL, while preserving privacy in joint recommendations. To evaluate GCPN, we construct the VFCL scenario using Amazon's public recommendation datasets. Experiments demonstrate that our method enhances the effectiveness of most tasks in CL and VFCL scenarios.