Proud to share our Federated learning Trilogy 🛡️

Hello, friends! Some exciting news to share today.

We have had their papers accepted at some of the top conferences, right away! Congrats, Sungwon Han, Sungwon Park, Fangzhao Wu, Xing Xie, Bin Zhu, and Meeyoung Cha 🎉 Let me proudly introduce to you our Federated Learning Trilogy 🛡️

1) The most recent paper, for ICCV’23, is about making Federated Learning more secure.
2) The second, for KDD’23, discusses how to protect Federated Learning on the client-side.
3) And the third, for ECCV’22, looks at Federated Learning without supervision.

Thank you for your hard work and dedication!

[1] ICCV’23: Towards Attack-Tolerant Federated Learning via Critical Parameter Analysis
[2] KDD’23: FedDefender: Client-Side Attack-Tolerant Federated Learning
[3] ECCV’22: FedX: Unsupervised Federated Learning with Cross Knowledge Distillation