The growing scale of data and models in machine learning calls for efficient optimization methods. Distributed optimization addresses this challenge, with compression mechanisms helping to reduce comm...
arXiv:2609. 11712v1 Announce Type: cross Abstract: In this paper, we investigate the generalization performance of distributed gradient descent algorithms in a reproducing kernel Hilbert space under a robust loss function $l_{\sigma}$.
By Jun-Yi Meng, Zheng-Chu Guo, Yuan Mao
arXiv:2605. 28335v2 Announce Type: replace Abstract: Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but it is highly vulnerable to Byzantine attacks.
By Shiyuan Zuo, Jiashuo Li, Rongfei Fan, Han Hu, Jie Xu
arXiv:2608. 06563v1 Announce Type: new Abstract: Machine learning and optimization have advanced together, with practical demands motivating new theory and theoretical breakthroughs enabling new applications.
By Grigory Malinovsky
arXiv:2607. 10970v1 Announce Type: new Abstract: Federated learning distributes data among $n$ clients, making it vulnerable to malicious attacks and data heterogeneity, which together pose challenges for robust learning.
By Zhi-Yong Wang, Hao Nan Sheng, Werner Stefan, Hing Cheung So, Linqi Song, Weitao Xu
Federated learning distributes data among $n$ clients, making it vulnerable to malicious attacks and data heterogeneity, which together pose challenges for robust learning. To tackle this issue, centered clipping and Huber aggregators have been exploited for Byzantine robustness.