FedPDD: A Privacy-preserving Double Distillation Framework for Cross-silo Federated Recommendation. (arXiv:2305.06272v2 [cs.IR] UPDATED)

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 arxiv collaborations cs.ir data deal distillation features federated learning framework platform platforms practical privacy protection recommendation regulations solution training