FedPDD: A Privacy-preserving Double Distillation Framework for Cross-silo Federated Recommendation

Cross-platform recommendation aims to improve recommendation accuracy by gathering heterogeneous features from different platforms. However, such cross-silo collaborations between platforms are restricted by increasingly stringent privacy protection regulations, thus data cannot be aggregated for training. Federated learning (FL) is a practical solution to deal with the data silo problem in recommendation scenarios. Existing cross-silo FL methods transmit model information to collaboratively build a global model by leveraging the data of overlapped users. However, in reality, the number of overlapped users …

accuracy collaborations cs.cr cs.dc cs.ir cs.lg data data silo deal distillation features federated learning framework platform platforms practical privacy protection recommendation regulations solution training