  With the growing concerns regarding user data privacy, Federated Recommender
System (FedRec) has garnered significant attention recently due to its
privacy-preserving capabilities. Existing FedRecs generally adhere to a
learning protocol in which a central server shares a global recommendation
model with clients, and participants achieve collaborative learning by
frequently communicating the model's public parameters. Nevertheless, this
learning framework has two drawbacks that limit its practical usability: (1) It
necessitates a global-sharing recommendation model; however, in real-world
scenarios, information related to the recommender model, including its
algorithm and parameters, constitutes the platforms' intellectual property.
Hence, service providers are unlikely to release such information actively. (2)
The communication costs of model parameter transmission are expensive since the
model parameters are usually high-dimensional matrices. With the model size
increasing, the communication burden will be the bottleneck for such
traditional FedRecs.
  Given the above limitations, this paper introduces a novel parameter
transmission-free federated recommendation framework that balances the
protection between users' data privacy and platforms' model privacy, namely
PTF-FedRec. Specifically, participants in PTF-FedRec collaboratively exchange
knowledge by sharing their predictions within a privacy-preserving mechanism.
Through this way, the central server can learn a recommender model without
disclosing its model parameters or accessing clients' raw data, preserving both
the server's model privacy and users' data privacy. Besides, since clients and
the central server only need to communicate prediction scores which are just a
few real numbers, the overhead is significantly reduced compared to traditional
FedRecs. The code is available
at\url{https://github.com/hi-weiyuan/PTF-FedRec}.
