3 papers
cs.CV2025
Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization
Zhuang Qi, Sijin Zhou, Lei Meng +3
Attribute bias in federated learning (FL) typically leads local models to optimize inconsistently due to the learning of non-causal associations, resulting degraded performance. Ex…
cs.CV2025
Federated Out-of-Distribution Generalization: A Causal Augmentation View
Runhui Zhang, Sijin Zhou, Zhuang Qi
Federated learning aims to collaboratively model by integrating multi-source information to obtain a model that can generalize across all client data. Existing methods often levera…
cs.IR2023
Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for Recommendation
Guoqiang Sun, Yibin Shen, Sijin Zhou +6
Cross-domain recommendation has attracted increasing attention from industry and academia recently. However, most existing methods do not exploit the interest invariance between do…