Data sharing-based approach for Federated Learning tasks on Edge Devices
DOI:
https://doi.org/10.5753/jbcs.2025.3682Keywords:
Federated Learning, non-IID data, distributed datasets, privacy-flexible environment, privacy-sensitive environment, edge devices, training and convergenceAbstract
Federated Learning (FL) enables edge devices to collaboratively train a global machine learning model. In this paradigm, the data is maintained on the devices themselves and a server is responsible for aggregating the parameters of the local models. However, the aggregated model may present convergence difficulties when the device data are non-independent and identically distributed (non-IID), that is, when they present a heterogeneous distribution. This work proposes an algorithm that extends data sharing-based solutions from the literature by considering privacy-flexible environment, where users agree to share a small percentage of their private, and privacy-sensitive environment, where it is assumed that the aggregator server has a set of public global data that is shared with users in the initial phase of the FL process. The proposed algorithm is evaluated in a distributed and centralized way considering a Human Activity Recognition (HAR) application. The results show that data sharing strategies indicate improved global model performance in non-IID scenarios.
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Copyright (c) 2025 Renan R. de Oliveira, Leandro A. Freitas, Waldir Moreira, Maria Ribeiro, Antonio Oliveira-Jr
This work is licensed under a Creative Commons Attribution 4.0 International License.
