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}.

翻译：随着用户数据隐私问题日益受到关注，联邦推荐系统（FedRec）因其隐私保护能力近年来备受瞩目。现有FedRec通常遵循一种学习协议：中央服务器与客户端共享一个全局推荐模型，参与者通过频繁传递模型的公开参数实现协作学习。然而，这种学习框架存在两个限制其实际可用性的缺陷：（1）它需要全局共享推荐模型，但在现实场景中，推荐模型的相关信息（包括算法和参数）构成平台的知识产权，因此服务提供商不太可能主动公开此类信息。（2）模型参数传输的通信开销高昂，因为模型参数通常是高维矩阵。随着模型规模增大，通信负担将成为传统FedRec的瓶颈。鉴于上述限制，本文提出一种新颖的无参数传输联邦推荐框架——PTF-FedRec，在用户数据隐私与平台模型隐私之间实现平衡。具体而言，PTF-FedRec中的参与者通过共享隐私保护机制下的预测结果进行知识交换。通过这种方式，中央服务器无需公开其模型参数或访问客户端的原始数据即可学习推荐模型，从而同时保护服务器模型隐私与用户数据隐私。此外，由于客户端与中央服务器只需通信预测分数（即少量实数），其开销相较于传统FedRec显著降低。代码开源地址为\url{https://github.com/hi-weiyuan/PTF-FedRec}。