  Click-through rate (CTR) prediction plays an important role in online
advertising platforms. Most existing methods use data from the advertising
platform itself for CTR prediction. As user behaviors also exist on many other
platforms, e.g., media platforms, it is beneficial to further exploit such
complementary information for better modeling user interest and for improving
CTR prediction performance. However, due to privacy concerns, data from
different platforms cannot be uploaded to a server for centralized model
training. Vertical federated learning (VFL) provides a possible solution which
is able to keep the raw data on respective participating parties and learn a
collaborative model in a privacy-preserving way. However, traditional VFL
methods only utilize aligned data with common keys across parties, which
strongly restricts their application scope. In this paper, we propose FedUD,
which is able to exploit unaligned data, in addition to aligned data, for more
accurate federated CTR prediction. FedUD contains two steps. In the first step,
FedUD utilizes aligned data across parties like traditional VFL, but it
additionally includes a knowledge distillation module. This module distills
useful knowledge from the guest party's high-level representations and guides
the learning of a representation transfer network. In the second step, FedUD
applies the learned knowledge to enrich the representations of the host party's
unaligned data such that both aligned and unaligned data can contribute to
federated model training. Experiments on two real-world datasets demonstrate
the superior performance of FedUD for federated CTR prediction.
