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.

翻译：随着对用户数据隐私的日益关注，联邦推荐系统（FedRec）因其隐私保护能力而受到广泛关注。现有联邦推荐系统通常遵循一种学习协议，即中央服务器与客户端共享全局推荐模型，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成本高昂，因为模型参数通常是高维矩阵。随着模型规模增大，通信负担将成为此类传统联邦推荐系统的瓶颈。针对上述局限性，本文提出一种新颖的免参数传输联邦推荐框架——PTF-FedRec，该框架平衡了用户数据隐私与平台模型隐私的保护。具体而言，PTF-FedRec中的参与者通过隐私保护机制共享其预测结果来协作交换知识。通过这种方式，中央服务器可以在不泄露其模型参数或访问客户端原始数据的情况下学习推荐模型，从而同时保护服务器的模型隐私和用户的数据隐私。此外，由于客户端和中央服务器仅需传输预测分数（仅为少量实数），与传统联邦推荐系统相比，开销显著降低。