HFedCWA: heterogeneous federated learning algorithm based on contribution-weighted aggregation

IF 3.4 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jiawei Du, Huaijun Wang, Junhuai Li, Kan Wang, Rong Fei
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引用次数: 0

Abstract

The aim of heterogeneous federated learning (HFL) is to address the issues of data heterogeneity, computational resource disparity, and model generalizability and security in federated learning (FL). To facilitate the collaborative training of data and enhance the predictive performance of models, a heterogeneous federated learning algorithm based on contribution-weighted aggregation (HFedCWA) is proposed in this paper. First, weights are assigned on the basis of the distribution differences and quantities of heterogeneous device data, and a contribution-based weighted aggregation method is introduced to dynamically adjust weights and balance data heterogeneity. Second, personalized strategies based on regularization are formulated for heterogeneous devices with different weights, enabling each device to participate in the overall task in an optimal manner. Differential privacy methods are concurrently utilized in FL training to further enhance the security of the system. Finally, experiments are conducted under various data heterogeneity scenarios using the MNIST and CIFAR10 datasets, and the results demonstrate that the HFedCWA can effectively improve the model’s generalizability ability and adaptability to heterogeneous data, thereby enhancing the overall efficiency and performance of the HFL system.

Abstract Image

HFedCWA:基于贡献加权聚合的异构联邦学习算法
异构联邦学习(HFL)的目标是解决联邦学习中存在的数据异构性、计算资源差异、模型泛化和安全性等问题。为了促进数据的协同训练,提高模型的预测性能,本文提出了一种基于贡献加权聚合的异构联邦学习算法。首先,根据异构设备数据的分布差异和数量分配权重,引入基于贡献的加权聚合方法,动态调整权重,平衡数据异构性;其次,针对不同权重的异构设备制定基于正则化的个性化策略,使各设备以最优方式参与整体任务。在FL训练中同时采用差分隐私方法,进一步提高系统的安全性。最后,利用MNIST和CIFAR10数据集在各种数据异构场景下进行了实验,结果表明,HFedCWA可以有效提高模型的泛化能力和对异构数据的适应性,从而提高HFL系统的整体效率和性能。
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来源期刊
Applied Intelligence
Applied Intelligence 工程技术-计算机:人工智能
CiteScore
6.60
自引率
20.80%
发文量
1361
审稿时长
5.9 months
期刊介绍: With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.
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