Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data

no code implementations • 25 Mar 2024 • Xinting Liao, Weiming Liu, Chaochao Chen, Pengyang Zhou, Fengyuan Yu, Huabin Zhu, Binhui Yao, Tao Wang, Xiaolin Zheng, Yanchao Tan

The former indicates that representation collapse in local model will subsequently impact the global model and other local models.

Federated Learning Federated Unsupervised Learning

Reducing Communication for Split Learning by Randomized Top-k Sparsification

no code implementations • 29 May 2023 • Fei Zheng, Chaochao Chen, Lingjuan Lyu, Binhui Yao

However, communication efficiency is still a crucial issue for split learning.

Quantization Vertical Federated Learning

PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation

no code implementations • 11 May 2023 • Xinting Liao, Weiming Liu, Xiaolin Zheng, Binhui Yao, Chaochao Chen

Privacy-preserving cross-domain recommendation (PPCDR) refers to preserving the privacy of users when transferring the knowledge from source domain to target domain for better performance, which is vital for the long-term development of recommender systems.

Generative Adversarial Network Privacy Preserving +1

Making Split Learning Resilient to Label Leakage by Potential Energy Loss

no code implementations • 18 Oct 2022 • Fei Zheng, Chaochao Chen, Binhui Yao, Xiaolin Zheng

As a practical privacy-preserving learning method, split learning has drawn much attention in academia and industry.

Privacy Preserving