Federated learning (FL) has become a prominent paradigm for collaborative model training while ensuring data privacy. However, in resource-constrained environments, such as the Internet of Things (IoT), FL faces a distinct challenge from Lazybone attackers, who compromise system performance by providing low-quality data or conducting minimal local training to reduce their computational burden. In this paper, we propose Fedeval, a novel multi-dimensional evaluation framework designed to defend against Lazybone attacks. Fedeval leverages a server-side base validation dataset and a base model to assess the quality and relevance of client contributions through gradient inversion, and it compares client-uploaded gradients with an honest baseline to detect training inconsistencies. By assigning adaptive importance scores based on client contributions, Fedeval enhances the robustness of FL by mitigating the impact of non-contributing participants. We also provide a theoretical analysis of Fedeval’s convergence properties and validate its effectiveness through extensive experiments on four datasets and two attack scenarios. Our results demonstrate that Fedeval significantly accelerates convergence and improves accuracy by up to 13% compared to traditional methods.

Hao Wang

Haoran Zhang

Lu Wang

Shichang Xuan

Qian Zhang

ACM Transactions on Sensor Networks

ACM TRANSACTIONS ON SENSOR NETWORKS (TOSN) 是 ACM 在传感器网络跨学科领域的核心出版物，涵盖从信号处理、网络和协议、嵌入式系统、信息管理到分布式算法的广泛学科。它涵盖了引入新概念、技术、分析或架构的研究贡献，以及报告新工具和系统开发或具有高影响力、创新应用的经验和实验的应用贡献。这些交易特别关注对传感器网络系统方法的贡献以及基本贡献。

ACM Transactions on Sensor Networks (TOSN) is a key publication by ACM in the interdisciplinary field of sensor networks, covering a wide range of subjects from signal processing, networking and protocols, embedded systems, information management, to distributed algorithms. It accepts research contributions that introduce new concepts, technologies, analyses, or architectures, as well as reports on the development of new tools and systems, or empirical and experimental contributions with high impact and innovative applications. The journal particularly emphasizes contributions to sensor network system methodologies and foundational research.

《ACM Transactions on Sensor Networks》是ACM在传感器网络跨学科领域的重要出版物，涵盖信号处理、网络与协议、嵌入式系统、信息管理以及分布式算法等多个学科。期刊接受关于新概念、技术、分析或架构的研究贡献，以及新工具和系统开发的报告，或具有高影响力和创新应用的经验和实验贡献。特别关注对传感器网络系统方法的贡献及其基本研究。

Category	Quartile
计算机科学	4
计算机科学, 计算机信息系统	4
计算机科学, 电信学	4

Fedeval: Defending Against Lazybone Attack via Multi-dimension Evaluation in Federated Learning

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