Title: Privacy-preserved federated learning for internet of things with multi-round sparse aggregation

Authors: Jiao Zhang; Xueting Huang; Xiong Li; Kai Jin; Dacheng He; Wei Liang

Addresses: College of Information and Intelligence, Hunan Agricultural University, Changsha, 410128, China ' School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China ' School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China ' School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China ' School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China ' School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China

Abstract: This paper addresses the challenges of privacy-preserved federated learning (PPFL) in the internet of things (IoT) by introducing Multi-SparseAgg, an innovative framework designed for efficient, secure multi-round aggregation. Existing secure aggregation protocols in PPFL struggle with scalability, latency, and the communication overhead associated with frequent model updates across numerous, resource-limited IoT devices. Multi-SparseAgg tackles these challenges by employing sparse neural networks optimised with binary masks, significantly reducing communication costs without sacrificing model accuracy. A one-time setup phase generates reusable secrets, eliminating the need for costly reinitialisation in each round and enabling robust aggregation even with intermittent client participation. Experimental results on a benchmark dataset demonstrate that Multi-SparseAgg significantly lowers communication costs on the client side by 8.2% to 69.1% and on the server side by 7.1% to 28.6%, compared to baseline methods. It also reduces computational overhead by 21.1% to 77.6% while preserving model accuracy and ensuring fast convergence.

Keywords: privacy-preserved federated learning; PPFL; sparse subnetwork; secure aggregation; multi-round aggregation.

DOI: 10.1504/IJES.2025.149251

International Journal of Embedded Systems, 2025 Vol.18 No.2, pp.113 - 124

Received: 29 Nov 2024
Accepted: 17 Jun 2025
Published online: 20 Oct 2025 *

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