FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation Learning

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

FedMVT: Semi-supervised Vertical Federated Learning with MultiView Training

Federated learning allows many parties to collaboratively build a model without exposing data. Particularly, vertical federated learning (VFL) enables parties to build a robust shared machine learning model based upon distributed features about the same samples. However, VFL requires all parties to share a sufficient amount of overlapping samples. In reality, the set of overlapping samples may be small, leaving the majority of the non-overlapping data unutilized. In this paper, we propose Federated Multi-View Training (FedMVT), a semi-supervised learning approach that improves the performance of VFL with limited overlapping samples. FedMVT estimates representations for missing features and predicts pseudo-labels for unlabeled samples to expand training set, and trains three classifiers jointly based upon different views of the input to improve model's representation learning. FedMVT does not require parties to share their original data and model parameters, thus preserving data privacy. We conduct experiments on the NUS-WIDE and the CIFAR10. The experimental results demonstrate that FedMVT significantly outperforms vanilla VFL that only utilizes overlapping samples, and improves the performance of the local model in the party that owns labels.Comment: International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with IJCAI 2020 (FL-IJCAI'20

An integrated framework for user modeling using deep learning on a data monetization platform

This paper presents a novel approach to privacy-preserving user modeling for digital marketing campaigns using deep learning techniques on a data monetization platform, which enables users to maintain control over their personal data while allowing marketers to identify suitable target audiences for their campaigns. The system comprises of several stages, starting with the use of representation learning on hyperbolic space to capture the latent user interests across multiple data sources with hierarchical structures. Next, Generative Adversarial Networks are employed to generate synthetic user interests from these embeddings. To ensure the privacy of user data, a Federated Learning technique is implemented for decentralized user modeling training, without sharing data with marketers. Lastly, a targeting strategy based on recommendation system is constructed to leverage the learned user interests for identifying the optimal target audience for digital marketing campaigns. Overall, the proposed approach provides a comprehensive solution for privacy-preserving user modeling for digital marketing.publishersversionpublishe