A Fair Resource Allocation Scheme in Federated Learning
-
摘要: 联邦学习(federated learning, FL)是一种可用于解决数据孤岛问题的分布式机器学习框架，多个参与方在保持数据本地私有的情况下协作训练一个共同模型.但是，传统的联邦学习没有考虑公平性的问题，在实际场景中，参与者之间的数据具有高度异构和数据量差距较大的特点，常规的聚合操作会不经意地偏向一些设备，使得最终聚合模型在不同参与者数据上的准确率表现出较大差距.针对这一问题，提出了一种有效的公平算法，称为α-FedAvg.它可以使聚合模型更公平，即其在所有参与者本地数据上的准确率分布更均衡.同时，给出了确定参数α的方法，能够在尽可能保证聚合模型性能的情况下提升其公平性.最后，在MNIST和CIFAR-10数据集上进行了实验和性能分析，并在多个数据集上与其他3种公平方案进行了对比.实验结果表明：相较于已有算法，所提方案在公平性和有效性上达到了更好的平衡.Abstract: Federated learning (FL) is a distributed machine learning framework that can be used to solve the data silos problem. Using the framework multiple participants collaborate to train a global model while keeping the data locally private. However, the traditional federated learning ignores the importance of fairness, which may influence the quality of the trained global model. As different participants hold different magnitudes data which are highly heterogeneous, traditional training methods such as natively minimizing an aggregate loss function may disproportionately advantage or disadvantage some of the devices. Thus the final global model shows a large gap in accuracy on different participants’ data. To train a global model in a more fair manner, we propose a fairness method called α-FedAvg. Using α-FedAvg participants can obtain a global model. That is, the final global model trained by all participants allows a more balanced distribution of accuracy on the participants’ local data. Meanwhile, we devise a method to yield the parameter α, which can improve the fairness of the global model while ensuring its performance. To evaluate our scheme, we test the global model on MNIST and CIFAR-10 datasets. Meanwhile, we compare α-FedAvg with other three fairness schemes on multiple datasets. Compared with existing schemes, our scheme achieves a better balance between fairness and effectiveness.
-
Keywords:
- federated learning (FL) /
- fairness /
- effectiveness /
- trade off /
- resource allocation
-
-
期刊类型引用(7)
1. 张淑芬，张宏扬，任志强，陈学斌. 联邦学习的公平性综述. 计算机应用. 2025(01): 1-14 . 
百度学术
2. 朱智韬，司世景，王健宗，程宁，孔令炜，黄章成，肖京. 联邦学习的公平性研究综述. 大数据. 2024(01): 62-85 . 百度学术
3. 李锦辉，吴毓峰，余涛，潘振宁. 数据孤岛下基于联邦学习的用户电价响应刻画及其应用. 电力系统保护与控制. 2024(06): 164-176 . 百度学术
4. 刘新，刘冬兰，付婷，王勇，常英贤，姚洪磊，罗昕，王睿，张昊. 基于联邦学习的时间序列预测算法. 山东大学学报(工学版). 2024(03): 55-63 . 百度学术
5. 赵泽华，梁美玉，薛哲，李昂，张珉. 基于数据质量评估的高效强化联邦学习节点动态采样优化. 智能系统学报. 2024(06): 1552-1561 . 百度学术
6. 杨秀清，彭长根，刘海，丁红发，汤寒林. 基于数据质量评估的公平联邦学习方案. 计算机与数字工程. 2022(06): 1278-1285 . 百度学术
7. 黎志鹏. 高可靠的联邦学习在图神经网络上的聚合方法. 工业控制计算机. 2022(10): 85-87+90 . 百度学术
其他类型引用(10)
计量
- 文章访问数: 380
- HTML全文浏览量: 8
- PDF下载量: 219
- 被引次数: 17
下载:
