PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender System
Wei Yuan, Chaoqun Yang, Liang Qu, Quoc Viet Hung Nguyen, Guanhua Ye,, Hongzhi Yin

TL;DR
This paper introduces PTF-FSR, a federated sequential recommendation system that enhances privacy and reduces communication costs by transmitting only prediction results, enabling the use of larger models without sharing parameters.
Contribution
The paper proposes a novel parameter transmission-free federated recommendation framework that protects model and data privacy while supporting larger, more complex models.
Findings
Effective on multiple datasets
Supports ID-based and ID-free models
Reduces communication overhead
Abstract
Sequential recommender systems have made significant progress. Recently, due to increasing concerns about user data privacy, some researchers have implemented federated learning for sequential recommendation, a.k.a., Federated Sequential Recommender Systems (FedSeqRecs), in which a public sequential recommender model is shared and frequently transmitted between a central server and clients to achieve collaborative learning. Although these solutions mitigate user privacy to some extent, they present two significant limitations that affect their practical usability: (1) They require a globally shared sequential recommendation model. However, in real-world scenarios, the recommendation model constitutes a critical intellectual property for platform and service providers. Therefore, service providers may be reluctant to disclose their meticulously developed models. (2) The communication…
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Taxonomy
TopicsData Management and Algorithms · Web Data Mining and Analysis · Recommender Systems and Techniques
Methodstravel james
