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Yankai Lv, Haiyan Ding, Hao Wu, Yiji Zhao and Lei Zhang
Federated learning (FL) is an emerging decentralized machine learning framework enabling private global model training by collaboratively leveraging local client data without transferring it centrally. Unlike traditional distributed optimization, FL trai...
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Qianqian Tong, Guannan Liang, Jiahao Ding, Tan Zhu, Miao Pan and Jinbo Bi
Regularized sparse learning with the l0
l
0
-norm is important in many areas, including statistical learning and signal processing. Iterative hard thresholding (IHT) methods are the state-of-the-art for nonconvex-constrained sparse learning due to their ...
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Zheyi Chen, Weixian Liao, Pu Tian, Qianlong Wang and Wei Yu
Distributed machine learning paradigms have benefited from the concurrent advancement of deep learning and the Internet of Things (IoT), among which federated learning is one of the most promising frameworks, where a central server collaborates with loca...
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