Article: Cross-node knowledge transfer and generalisation based on federated meta-learning in fog computing Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.6 pp.90 - 107 Abstract: Fog computing environments comprise numerous heterogeneous and resource-constrained nodes, resulting in significant generalisation deficiencies in conventional machine learning models when addressing cross-node tasks characterised by non-independent and identically distributed data and limited labelling resources. This research offers a federated meta-learning architecture designed for fog computing, with the objective of facilitating rapid, privacy-preserving knowledge transfer and task adaptation between nodes. This framework combines the distributed model training paradigm of federated learning with the 'learning to learn' mechanism of meta-learning. We performed comprehensive tests on public datasets to simulate standard non-IID data settings in fog computing. Results indicate that, in comparison to conventional federated learning baselines, our methodology attains an average accuracy improvement of roughly 3.7% on novel tasks, while markedly enhancing model convergence and task adaptation efficiency. This suggests that the proposed approach significantly improves learning and generalisation capabilities for resource-limited nodes in fog computing settings while addressing unfamiliar tasks. Inderscience Publishers - linking academia, business and industry through research

Title: Cross-node knowledge transfer and generalisation based on federated meta-learning in fog computing

Authors: Qiuming Zhang; Jing Luo

Addresses: School of Computer Science, China University of Geosciences, Wuhan, 430074, China ' Library and Archives, Wuhan Vocational College of Software and Engineering, Wuhan, 430205, China

Abstract: Fog computing environments comprise numerous heterogeneous and resource-constrained nodes, resulting in significant generalisation deficiencies in conventional machine learning models when addressing cross-node tasks characterised by non-independent and identically distributed data and limited labelling resources. This research offers a federated meta-learning architecture designed for fog computing, with the objective of facilitating rapid, privacy-preserving knowledge transfer and task adaptation between nodes. This framework combines the distributed model training paradigm of federated learning with the 'learning to learn' mechanism of meta-learning. We performed comprehensive tests on public datasets to simulate standard non-IID data settings in fog computing. Results indicate that, in comparison to conventional federated learning baselines, our methodology attains an average accuracy improvement of roughly 3.7% on novel tasks, while markedly enhancing model convergence and task adaptation efficiency. This suggests that the proposed approach significantly improves learning and generalisation capabilities for resource-limited nodes in fog computing settings while addressing unfamiliar tasks.

Keywords: federated learning; meta-learning; fog computing; non-independent identically distributed; non-IID.

DOI: 10.1504/IJICT.2026.151557

International Journal of Information and Communication Technology, 2026 Vol.27 No.6, pp.90 - 107

Received: 26 Oct 2025
Accepted: 25 Nov 2025
Published online: 06 Feb 2026 *

Title: Cross-node knowledge transfer and generalisation based on federated meta-learning in fog computing

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