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Ying-Hsun Lai, Shin-Yeh Chen, Wen-Chi Chou, Hua-Yang Hsu and Han-Chieh Chao
Federated learning trains a neural network model using the client?s data to maintain the benefits of centralized model training while maintaining their privacy. However, if the client data are not independently and identically distributed (non-IID) becau...
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Kavitha Srinivasan, Sainath Prasanna, Rohit Midha, Shraddhaa Mohan
Pág. 1 - 20
Advances have been made in the field of Machine Learning showing that it is an effective tool that can be used for solving real world problems. This success is hugely attributed to the availability of accessible data which is not the case for many fields...
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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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Sumit Rai, Arti Kumari and Dilip K. Prasad
Federated learning promises an elegant solution for learning global models across distributed and privacy-protected datasets. However, challenges related to skewed data distribution, limited computational and communication resources, data poisoning, and ...
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Ahmed A. Al-Saedi, Veselka Boeva and Emiliano Casalicchio
Federated Learning (FL) provides a promising solution for preserving privacy in learning shared models on distributed devices without sharing local data on a central server. However, most existing work shows that FL incurs high communication costs. To ad...
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Qianqian Tong, Guannan Liang, Jiahao Ding, Tan Zhu, Miao Pan and Jinbo Bi
Regularized sparse learning with the l0
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-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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