  Cross-domain Sequential Recommendation (CSR) which leverages user sequence
data from multiple domains has received extensive attention in recent years.
However, the existing CSR methods require sharing origin user data across
domains, which violates the General Data Protection Regulation (GDPR). Thus, it
is necessary to combine federated learning (FL) and CSR to fully utilize
knowledge from different domains while preserving data privacy. Nonetheless,
the sequence feature heterogeneity across different domains significantly
impacts the overall performance of FL. In this paper, we propose FedDCSR, a
novel federated cross-domain sequential recommendation framework via
disentangled representation learning. Specifically, to address the sequence
feature heterogeneity across domains, we introduce an approach called
inter-intra domain sequence representation disentanglement (SRD) to disentangle
the user sequence features into domain-shared and domain-exclusive features. In
addition, we design an intra domain contrastive infomax (CIM) strategy to learn
richer domain-exclusive features of users by performing data augmentation on
user sequences. Extensive experiments on three real-world scenarios demonstrate
that FedDCSR achieves significant improvements over existing baselines.
