Title: A theoretical framework for integrating federated learning and transfer learning: advancing optimisation in decentralised systems

Authors: Mohammed Abdul Wajeed; Annavarapu Chandra Sekhara Rao

Addresses: Department of Computer Science and Engineering, Lords Institute of Engineering and Technology, Hyderabad, Telangana, India ' Department of Computer Science and Engineering, Indian Institute of Technology (ISM), Dhanbad, Jharkhand, India

Abstract: Federated Learning (FL) has transformed decentralised model training by enabling collaborative learning while protecting data privacy. Key challenges include non-iid data distributions, slow convergence and limited understanding of combining FL with other paradigms. This research introduces a theoretical framework establishing foundations for incorporating Transfer Learning (TL) into FL to address these issues. The Federated Transfer Optimisation (FTO) framework expands FL optimisation theories by introducing transfer-invariant initialisation metrics for efficient use of pre-trained models. We introduce a Transfer Learning Augmented Loss (TLAL) function combining global objectives and local transfer dynamics to control knowledge retention during fine-tuning. The framework presents adaptive task-alignment kernels to balance global and client-specific objectives in heterogeneous scenarios. Experimental evaluations on text classification data sets show FTO achieves better accuracy, reduced communication overhead and faster convergence compared to existing FL methods. This study provides a principled basis for integrating TL, enabling efficient learning systems for privacy-sensitive applications.

Keywords: federated learning; transfer learning; federated transfer optimisation; distributed optimisation; adaptive task-alignment kernels; transfer learning augmented loss; TLAL; integrate federated transfer learning; text classification.

DOI: 10.1504/IJCAT.2026.154034

International Journal of Computer Applications in Technology, 2026 Vol.78 No.4, pp.336 - 347

Received: 20 Nov 2024
Accepted: 28 Aug 2025
Published online: 10 Jun 2026 *

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