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 is often very small, thus largely limiting the performance of
such approaches. Moreover, transmitting model information during training
requires high communication costs and may cause serious privacy leakage. In
this paper, we propose a novel privacy-preserving double distillation framework
named FedPDD for cross-silo federated recommendation, which efficiently
transfers knowledge when overlapped users are limited. Specifically, our double
distillation strategy enables local models to learn not only explicit knowledge
from the other party but also implicit knowledge from its past predictions.
Moreover, to ensure privacy and high efficiency, we employ an offline training
scheme to reduce communication needs and privacy leakage risk. In addition, we
adopt differential privacy to further protect the transmitted information. The
experiments on two real-world recommendation datasets, HetRec-MovieLens and
Criteo, demonstrate the effectiveness of FedPDD compared to the
state-of-the-art approaches.

本文提出了一种用于跨平台推荐的隐私保护双蒸馏框架，其通过双重蒸馏策略实现了跨平台本地模型知识转移，采用离线训练方案降低了通信需求和隐私泄露风险，并采用差分隐私进一步保护传输的信息。与现有领先方法相比，本文的方法在两个真实数据集上实现了更好的效果。