PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender System
CoRR(2024)
摘要
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 costs are
high as they correlate with the number of model parameters. This becomes
particularly problematic as the current FedSeqRec will be inapplicable when
sequential recommendation marches into a large language model era.
To overcome the above challenges, this paper proposes a parameter
transmission-free federated sequential recommendation framework (PTF-FSR),
which ensures both model and data privacy protection to meet the privacy needs
of service providers and system users alike. Furthermore, since PTF-FSR only
transmits prediction results under privacy protection, which are independent of
model sizes, this new federated learning architecture can accommodate more
complex and larger sequential recommendation models. Extensive experiments
conducted on three widely used recommendation datasets, employing various
sequential recommendation models from both ID-based and ID-free paradigms,
demonstrate the effectiveness and generalization capability of our proposed
framework.
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