Version 1
: Received: 28 August 2024 / Approved: 29 August 2024 / Online: 29 August 2024 (09:18:48 CEST)
How to cite:
Liberti, F.; Berardi, D.; Martini, B. Federated Learning in Dynamic and Heterogeneous Environments: Advantages, Performances, and Privacy Problems. Preprints2024, 2024082125. https://doi.org/10.20944/preprints202408.2125.v1
Liberti, F.; Berardi, D.; Martini, B. Federated Learning in Dynamic and Heterogeneous Environments: Advantages, Performances, and Privacy Problems. Preprints 2024, 2024082125. https://doi.org/10.20944/preprints202408.2125.v1
Liberti, F.; Berardi, D.; Martini, B. Federated Learning in Dynamic and Heterogeneous Environments: Advantages, Performances, and Privacy Problems. Preprints2024, 2024082125. https://doi.org/10.20944/preprints202408.2125.v1
APA Style
Liberti, F., Berardi, D., & Martini, B. (2024). Federated Learning in Dynamic and Heterogeneous Environments: Advantages, Performances, and Privacy Problems. Preprints. https://doi.org/10.20944/preprints202408.2125.v1
Chicago/Turabian Style
Liberti, F., Davide Berardi and Barbara Martini. 2024 "Federated Learning in Dynamic and Heterogeneous Environments: Advantages, Performances, and Privacy Problems" Preprints. https://doi.org/10.20944/preprints202408.2125.v1
Abstract
Federated Learning (FL) represents a promising distributed learning methodology, particularly suitable for dynamic and heterogeneous environments characterized by the presence of Internet of Things (IoT) devices and Edge Computing infrastructures. In this context, FL allows you to train machine learning models directly on edge devices, mitigating data privacy concerns and reducing latency due to transmitting data to central servers. However, the heterogeneity of computational resources, the variability of network connections, and the mobility of IoT devices pose significant challenges to the efficient implementation of FL. This work explores advanced techniques for dynamic model adaptation and heterogeneous data management in edge computing scenarios, proposing innovative solutions to improve the robustness and efficiency of federated learning. We present an innovative solution based on Kubernetes which enables the fast application of FL models to Heterogeneous Architectures. Experimental results demonstrate that our proposals can improve the performance of FL in IoT and edge environments, offering new perspectives for the practical implementation of decentralized intelligent systems.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
