Knowledge Commons of Institute of Automation,CAS
Overhead-free Noise-tolerant Federated Learning: A New Baseline | |
Shiyi Lin1; Deming Zhai1; Feilong Zhang1; Junjun Jiang1; Xianming Liu1; Xiangyang Ji2 | |
发表期刊 | Machine Intelligence Research
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ISSN | 2731-538X |
2024 | |
卷号 | 21期号:3页码:526-537 |
摘要 | Federated learning (FL) is a promising decentralized machine learning approach that enables multiple distributed clients to train a model jointly while keeping their data private. However, in real-world scenarios, the supervised training data stored in local clients inevitably suffer from imperfect annotations, resulting in subjective, inconsistent and biased labels. These noisy labels can harm the collaborative aggregation process of FL by inducing inconsistent decision boundaries. Unfortunately, few attempts have been made towards noise-tolerant federated learning, with most of them relying on the strategy of transmitting overhead messages to assist noisy labels detection and correction, which increases the communication burden as well as privacy risks. In this paper, we propose a simple yet effective method for noise-tolerant FL based on the well-established co-training framework. Our method leverages the inherent discrepancy in the learning ability of the local and global models in FL, which can be regarded as two complementary views. By iteratively exchanging samples with their high confident predictions, the two models “teach each other” to suppress the influence of noisy labels. The proposed scheme enjoys the benefit of overhead cost-free and can serve as a robust and efficient baseline for noise-tolerant federated learning. Experimental results demonstrate that our method outperforms existing approaches, highlighting the superiority of our method. |
关键词 | Federated learning, noise-label learning, privacy-preserving machine learning, edge intelligence, distributed machine learning |
DOI | 10.1007/s11633-023-1449-1 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/56480 |
专题 | 学术期刊_Machine Intelligence Research |
作者单位 | 1.Department of Computer Science and Technology, Harbin Institute of Technology, Harbin 150000, China 2.Department of Automation, Tsinghua University, Beijing 100084, China |
推荐引用方式 GB/T 7714 | Shiyi Lin,Deming Zhai,Feilong Zhang,et al. Overhead-free Noise-tolerant Federated Learning: A New Baseline[J]. Machine Intelligence Research,2024,21(3):526-537. |
APA | Shiyi Lin,Deming Zhai,Feilong Zhang,Junjun Jiang,Xianming Liu,&Xiangyang Ji.(2024).Overhead-free Noise-tolerant Federated Learning: A New Baseline.Machine Intelligence Research,21(3),526-537. |
MLA | Shiyi Lin,et al."Overhead-free Noise-tolerant Federated Learning: A New Baseline".Machine Intelligence Research 21.3(2024):526-537. |
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MIR-2023-03-027.pdf(1816KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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