Work

Non-IID Degree Aware Adaptive Federated Learning Procedure Selection Scheme for Edge-Enabled IoT Network

Year: 2025

Type: article

Abstract: Due to the independent, identically distributed (non-IID) nature of IoT device data, the traditional federated learning (FL) procedure, where IoT devices train the deep model in parallel, suffers from a degradation in learning accuracy. To mitigate this problem, a sequential FL procedure has been proposed, in which IoT devices train the deep model in a serialized manner via a parameter server. However, this approach experiences a longer convergence time due to the lack of parallelism. In this paper, we propose an adaptive FL procedure selection (AFLS) scheme that selects an appropriate FL scheme, either the traditional or the sequential FL procedures, based on the degree of non-IID among IoT devices to achieve both the required learning accuracy and low convergence time. To further reduce the convergence time of the sequential FL procedure, we also introduce a device-to-device (D2D)-based sequential FL procedure. The evaluation results demonstrate that AFLS can reduce convergence time by up to 16% compared to the sequential FL procedure and improve learning accuracy by up to 6∼26% compared to the traditional FL procedure.

Source: Electronics

DOI: 10.3390/electronics14122331

Authors: Sanghui Lee, Jaewook Lee

Institution: Pukyong National University

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion