Personalized and sustainable federated learning in integrated Internet of Things✩

Xu Zheng; Yifu Zheng; Tingqi Wang; Chong Mu; Ke Yan; Ling Tian

doi:10.1016/j.dcan.2026.01.001

›› 2026, Vol. 12 ›› Issue (3) :441 -450. DOI: 10.1016/j.dcan.2026.01.001

research-article

Personalized and sustainable federated learning in integrated Internet of Things^✩

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^a School of Computer Science and Engineering, University of Electronic Science and Technology of China , Chengdu, 611731, China

^b KASH Institute of Electronics and Information Industry , Kashi, 844000, China

^* E-mail addresses: xzheng@uestc.edu.cn (X. Zheng),

zhengyifu0204@gmail.com (Y. Zheng),

twang33@gsu.edu (T. Wang),

muchong@uestc.edu.cn (C. Mu),

kyan@uestc.edu.cn (K. Yan),

lingtian@uestc.edu.cn (L. Tian).

Xu Zheng received the BSc, MS, and PhD degrees in computer science and technology from Harbin Institute of Technology, Harbin, China, in 2010, 2012 and 2017, respectively, and his second PhD degree in Computer Science from Georgia State University, USA, in 2018. He is currently an associate professor with School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China. His research interests include Internet of Things, federated learning and data privacy.

Yifu Zheng received the BSc degree in Computer Science and Technology from University of Electronic Science and Technology of China, Chengdu, China in 2025. He is currently pursuing his Master’s degree with School of Electronic and Computer Engineering at Peking University. His research interests include federated learning and Artificial Intelligence.

Tingqi Wang received the BSC and MS degrees in Computer Science and Technology from University of Electronic Science and Technology of China, Chengdu, China in 2022 and 2025, respectively. She is currently a PhD student in the Department of Computer Science at Georgia State University, USA. Her primary research interests include multi-agent Learning and artificial intelligence, focusing on developing robust and efficient learning paradigms.

Chong Mu received his BE degree in School of Software from Hefei University of Technology in 2020. He obtained his Ph.D. degree in the School of Information and Software Engineering, University of Electronic Science and Technology of China in 2024. He is currently a postdoctoral researcher at University of Electronic Science and Technology of China. His research interests include knowledge graph completion and inductive reasoning.

Ke Yan received the BS, MS, and PhD degrees from the University of Electronic Science and Technology of China, in 2012, 2015 and 2020, respectively. He is currently an associate research Fellow in the School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China. He has been a visiting scholar in Georgia State University from 2017 to 2019. His research interests include knowledge reasoning and artificial intelligence.

Ling Tian received the BS, MS, and PhD degrees from the School of Computer Science and Engineering, University of Electronic Science and Technology of China in 2003, 2006 and 2010, respectively. Ling Tian is a Full Professor with School of Computer Science and Engineering, University of Electronic Science and Technology of China. Her research interests include artificial intelligence and data processing.

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Abstract

Integrated Internet of Things (IoT) brings novel opportunities for pervasive smart services, as these systems allow for seamless information and resource sharing among IoT devices. Meanwhile, Federated Learning (FL) has emerged as a new framework for distributed deployment of machine learning models and a promising approach for implementing intelligent IoT systems. However, integrated IoT systems are usually composed of diverse IoT devices from different systems, and thus their ownership, roles, data distribution, and capabilities are heterogeneous. Current FL algorithms mainly focus on handling non-Independent and Identically Distributed (Non-IID) issues, but often result in reduced and unsustainable performance in integrated IoT systems. Therefore, we investigate in this paper the problem of personalized and sustainable FL in integrated IoT systems. First, we argue that different parties in integrated IoT are heterogeneous and limited in available resources for FL, and these parties are also selfish and expect rational outcomes during cooperation, which is essential for guaranteeing the sustainability of integrated IoT. Then, this paper provides a novel framework for device selection in FL. It first sets one instance of the model for each device, and iteratively selects devices to participate in model training based on the joint consideration of local model accuracy, similarity of parameters, and remaining resources per device. The proposed method guarantees the rational allocation of resources to maintain balanced model performance across all devices. In this way, the sustainability of the whole IoT system is improved such that no devices will suffer extreme resource exhaustion or poor performance. Finally, extensive evaluation is conducted to validate the advanced performance of the proposed method in integrated IoT systems.

Keywords

Federated learning / Integrated IoT / Fairness / Sustainability

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Xu Zheng, Yifu Zheng, Tingqi Wang, Chong Mu, Ke Yan, Ling Tian. Personalized and sustainable federated learning in integrated Internet of Things^✩. , 2026, 12 (3) : 441-450 DOI:10.1016/j.dcan.2026.01.001

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CRediT authorship contribution statement

Xu Zheng: Validation, Methodology, Formal analysis, Conceptualization; Yifu Zheng: Visualization, Validation, Methodology; Tingqi Wang: Writing – original draft, Validation, Methodology, Formal analysis; Chong Mu: Writing – original draft, Formal analysis, Conceptualization; Ke Yan: Writing – review & editing, Formal analysis, Conceptualization; Ling Tian: Writing – review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

This research work was partly supported by National Natural Science Foundation of China under grant No. 62372085, 61802050, Xinjiang Science and Technology Program (No.2022D01B185), and the China Postdoctoral Science Foundation under Grant Number 2025M781461.

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