Independent and Identically Distributed (IID) Data Assessment in Federated Learning

Mohamad Arafeh, Ahmad Hammoud, H. Otrok, A. Mourad, C. Talhi, Z. Dziong
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引用次数: 1

Abstract

Federated learning extends the centralized machine learning architecture by enabling data privacy for its providers. The distributed structure of the emerged federated architecture imposes a problem of the data being not independent and identically distributed (non-IID), which drastically affects the performance of the learning process. While the majority of the recent works in the federated learning domain have accepted this limitation, only a few scholars addressed the non-IID problem straightforwardly. Nevertheless, these works lack the fundamental analysis of the data’ IIDness, and/or contradict the privacy feature of the federated learning paradigm. In this paper, we focus on evaluating the harmony of the participants by studying their data distribution and calculating their level of compatibility. The devised tool, in this work, is an assessment technique integrated within the federated learning framework to analyze the data distribution among the trainers. Our proposed method is proven by experimenting with several scenarios, and results show that our utility can fairly assess the selected participants before initiating the learning process.
联邦学习中的独立与同分布数据评估
联邦学习通过为其提供者提供数据隐私来扩展集中式机器学习体系结构。出现的联邦体系结构的分布式结构带来了数据不是独立和相同分布(非iid)的问题,这极大地影响了学习过程的性能。虽然最近在联邦学习领域的大多数工作都接受了这一限制,但只有少数学者直接解决了非iid问题。然而,这些工作缺乏对数据id的基本分析,并且/或者与联邦学习范式的隐私特征相矛盾。在本文中,我们主要通过研究参与者的数据分布和计算参与者的兼容性水平来评估参与者的和谐性。在这项工作中,设计的工具是一种集成在联邦学习框架内的评估技术,用于分析训练者之间的数据分布。我们提出的方法通过几个场景的实验证明,结果表明我们的实用程序可以在开始学习过程之前公平地评估选定的参与者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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