Personalized federated learningData heterogeneityFairnessWeighting strategyFederated learning (FL) is an emerging paradigm of distributed machine learning that facilitates collaborative training of a global mode
@inproceedings{FedAS_CVPR24,author={Yang, Xiyuan and Huang, Wenke and Ye, Mang},title={FedAS: Bridging Inconsistency in Personalized Fedearated Learning},booktitle={CVPR},year={2024}}@article{FCCLPlus_TPAMI23,title={Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity ...
& Ding, B. pFL-Bench: a comprehensive benchmark for personalized federated learning. In 36th Conf. Neural Information Processing Systems Datasets and Benchmarks Track (2022). Chai, J. & Wang, X. Self-supervised fair representation learning without demographics. In Adv. Neural Information ...
Representation learning: serial-autoencoder for personalized recommendationFront. Comput. Sci. (IF 3.4)Yi Zhu, Yishuai Geng, Yun Li, Jipeng Qiang, Xindong Wu Pub Date: 2023-12-16 Recommendation rules to personalize itineraries for tourists in an unfamiliar cityAppl. Soft Comput. (IF 7.2)Ines ...
TitleAuthorsPublished inYearFilesNotesSupplementaries Extended PrivSR Towards privacy preserving social recommendation under personalized privacy settings Xuying Meng, Suhang Wang, Kai Shu, Jundong Li, Bo Chen, Huan Liu, Yujun Zhang WWWJ 2019 📒 FedMF Secure Federated Matrix Factorization Di Chai, ...
Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications. However, much less attention has been paid to the fairness-aware multi-objective optimization, which is indeed commonly seen in...
(2021). Hierarchical personalized federated learning for user modeling. In WWW(pp. 957–968). Yang, C., Wang, Q., Xu, M., Chen, Z., Bian, K., Liu, Y., Liu, X. (2021). Characterizing impacts of heteroge- neity in federated learning upon large-scale smartphone data. In WWW...
Our proposed framework, "FairDPFL-SCS: Fair Dynamic Personalized Federated Learning with Strategic Client Selection", marks a notable advancement in this domain. By integrating dynamic learning rate adjustments and a strategic client selection mechanism, our approach effectively mitigates the challenges ...
Personalized Federated Learning (FL) faces many challenges such as expensive communication costs, training-time adversarial attacks, and performance unfairness across devices. Recent developments witness a trade-off between a reference model and local models to achieve personalization. Following the avenue,...
we focus on learning fair representations of CF-based recommendations. We formulate this problem as an optimization task with two competing goals: embedding representations better meet accuracy requirements of recommendations, and simultaneously obfuscate information hidden in the embedding space, which is ...