Virual Oral Presentation Slide by Slide

IEEE/ICACT20260326 Slide.13        [Big Slide]	Oral Presentation
	Slide 13. Conclusion. In this work, we introduced FSANL, a single-stage, noise-robust federated learning framework that jointly addresses heterogeneous label noise and non-IID client drift without assuming clean clients. By unifying symmetric loss, active negative suppression, and global L1 regularization, FSANL achieves stable and robust optimization under severe noise conditions. Experiments on CIFAR-10 show that FSANL outperforms state-of-the-art methods, particularly when all clients are noisy, and noise intensity is high.

[Go to Next Slide]

Select Voice: Virtual Oral Presentation!!