  Cross-domain Recommendation (CDR) as one of the effective techniques in
alleviating the data sparsity issues has been widely studied in recent years.
However, previous works may cause domain privacy leakage since they necessitate
the aggregation of diverse domain data into a centralized server during the
training process. Though several studies have conducted privacy preserving CDR
via Federated Learning (FL), they still have the following limitations: 1) They
need to upload users' personal information to the central server, posing the
risk of leaking user privacy. 2) Existing federated methods mainly rely on
atomic item IDs to represent items, which prevents them from modeling items in
a unified feature space, increasing the challenge of knowledge transfer among
domains. 3) They are all based on the premise of knowing overlapped users
between domains, which proves impractical in real-world applications. To
address the above limitations, we focus on Privacy-preserving Cross-domain
Recommendation (PCDR) and propose PFCR as our solution. For Limitation 1, we
develop a FL schema by exclusively utilizing users' interactions with local
clients and devising an encryption method for gradient encryption. For
Limitation 2, we model items in a universal feature space by their description
texts. For Limitation 3, we initially learn federated content representations,
harnessing the generality of natural language to establish bridges between
domains. Subsequently, we craft two prompt fine-tuning strategies to tailor the
pre-trained model to the target domain. Extensive experiments on two real-world
datasets demonstrate the superiority of our PFCR method compared to the SOTA
approaches.
