Dual Class-Aware Contrastive Federated Semi-Supervised Learning

no code implementations • 16 Nov 2022 • Qi Guo, Yong Qi, Saiyu Qi, Di wu

To our knowledge, we are the first to present an FSSL method that utilizes only 10\% labeled clients, while still achieving superior performance compared to standard federated supervised learning, which uses all clients with labeled data.

FedMCSA: Personalized Federated Learning via Model Components Self-Attention

no code implementations • 23 Aug 2022 • Qi Guo, Yong Qi, Saiyu Qi, Di wu, Qian Li

Federated learning (FL) facilitates multiple clients to jointly train a machine learning model without sharing their private data.

Personalized Federated Learning

Stochastic Batch Augmentation with An Effective Distilled Dynamic Soft Label Regularizer

no code implementations • 27 Jun 2020 • Qian Li, Qingyuan Hu, Yong Qi, Saiyu Qi, Jie Ma, Jian Zhang

SBA stochastically decides whether to augment at iterations controlled by the batch scheduler and in which a ''distilled'' dynamic soft label regularization is introduced by incorporating the similarity in the vicinity distribution respect to raw samples.

Data Augmentation