Cross-domain sequential recommendation (CDSR) models users’ dynamic preferences by exploiting behavioral signals from multiple domains, but it faces challenges in data sparsity, domain heterogeneity, and privacy protection. Although federated learning enables privacy-preserving CDSR by keeping raw data local, existing methods often suffer from sparse representations, unstable cross-domain alignment, and severe utility degradation under uniform differential privacy. In this work, we propose FedSCOPE, a novel federated CDSR framework that addresses these challenges through three tightly coupled and explicitly aligned components. First, FedSCOPE enriches user and item representations via offline large language model (LLM)-generated semantic augmentation, mitigating sparsity while avoiding online LLM inference and the associated privacy and deployment risks. Second, it introduces an Intra- and Inter-Domain Decoupled Contrastive Learning mechanism that separates intra-domain personalization from inter-domain discrimination, enabling robust cross-domain alignment under heterogeneous data distributions. Third, FedSCOPE incorporates an adaptive personalized differential privacy strategy that dynamically allocates privacy budgets and clipping thresholds according to client-specific data characteristics, achieving a more favorable privacy–utility trade-off in federated environments. These components are jointly optimized within a secure federated learning framework. Extensive experiments on multiple real-world datasets demonstrate that FedSCOPE consistently outperforms state-of-the-art baselines, achieving higher recommendation accuracy, stronger cross-domain generalization, and improved privacy–utility balance.
Zhao et al. (Thu,) studied this question.
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