2025
Felipe Machado Schwanck, Marcos Tomazzoli Leipnitz, Joel Luís Carbonera, Juliano Araujo Wickboldt
A Framework for testing Federated Learning algorithms using an edge-like environment Journal Article
In: Future Generation Computer Systems, 166 , pp. 107626, 2025, ISSN: 0167-739X.
Abstract Links BibTeX Tags: Cloud Computing Federated Learning Fog & Edge Computing Machine Learning
@article{journal/fgcs/Schwanck25,
title = {A Framework for testing Federated Learning algorithms using an edge-like environment},
author = {Felipe Machado Schwanck and Marcos Tomazzoli Leipnitz and Joel Luís Carbonera and Juliano Araujo Wickboldt},
url = {https://www.sciencedirect.com/science/article/pii/S0167739X24005909},
doi = {10.1016/j.future.2024.107626},
issn = {0167-739X},
year = {2025},
date = {2025-05-01},
urldate = {2025-05-01},
journal = {Future Generation Computer Systems},
volume = {166},
pages = {107626},
abstract = {Federated Learning (FL) is a machine learning paradigm in which many clients cooperatively train a single centralized model while keeping their data private and decentralized. FL is commonly used in edge computing, which involves placing computer workloads (both hardware and software) as close as possible to the edge, where data are created and where actions are occurring, enabling faster response times, greater data privacy, and reduced data transfer costs. However, due to the heterogeneous data distributions/contents of clients, it is non-trivial to accurately evaluate the contributions of local models in global centralized model aggregation. This is an example of a major challenge in FL, commonly known as data imbalance or class imbalance. In general, testing and evaluating FL algorithms can be a very difficult and complex task due to the distributed nature of the systems. In this work, a framework is proposed and implemented to evaluate FL algorithms in a more easy and scalable way. This framework is evaluated over a distributed edge-like environment managed by a container orchestration platform (i.e. Kubernetes).},
keywords = {Cloud Computing, Federated Learning, Fog & Edge Computing, Machine Learning},
pubstate = {published},
tppubtype = {article}
}
Federated Learning (FL) is a machine learning paradigm in which many clients cooperatively train a single centralized model while keeping their data private and decentralized. FL is commonly used in edge computing, which involves placing computer workloads (both hardware and software) as close as possible to the edge, where data are created and where actions are occurring, enabling faster response times, greater data privacy, and reduced data transfer costs. However, due to the heterogeneous data distributions/contents of clients, it is non-trivial to accurately evaluate the contributions of local models in global centralized model aggregation. This is an example of a major challenge in FL, commonly known as data imbalance or class imbalance. In general, testing and evaluating FL algorithms can be a very difficult and complex task due to the distributed nature of the systems. In this work, a framework is proposed and implemented to evaluate FL algorithms in a more easy and scalable way. This framework is evaluated over a distributed edge-like environment managed by a container orchestration platform (i.e. Kubernetes).
2023
Joberto Sérgio Barbosa Martins, Tereza Cristina Melo Brito Carvalho, Rodrigo Moreira, Cristiano Bonato Both, Adnei Donatti, João H. Corrêa, José Augusto Suruagy Monteiro, Sand Luz Corrêa, Antônio Jorge Gomes Abelém, Moisés Renato Nunes Ribeiro, José Marcos Silva Nogueira, Luiz Claudio Schara Magalhães, Juliano Araujo Wickboldt, Tiago Coelho Ferreto, Ricardo Mello, Rafael Pasquini, Marcos Schwarz, Leobino Nascimento Sampaio, Daniel Fernandes Macedo, José Ferreira Rezende, Kleber Vieira Cardoso, Flávio Oliveira Silva
Enhancing Network Slicing Architectures With Machine Learning, Security, Sustainability and Experimental Networks Integration Journal Article
In: IEEE Access, 11 , pp. 69144-69163, 2023.
Abstract Links BibTeX Tags: Experimental Approach Machine Learning Network Orchestration Network Security Network Virtualization & Slicing
@article{journal/access/MartinsSFI223,
title = {Enhancing Network Slicing Architectures With Machine Learning, Security, Sustainability and Experimental Networks Integration},
author = {Joberto Sérgio Barbosa Martins and Tereza Cristina Melo Brito Carvalho and Rodrigo Moreira and Cristiano Bonato Both and Adnei Donatti and João H. Corrêa and José Augusto Suruagy Monteiro and Sand Luz Corrêa and Antônio Jorge Gomes Abelém and Moisés Renato Nunes Ribeiro and José Marcos Silva Nogueira and Luiz Claudio Schara Magalhães and Juliano Araujo Wickboldt and Tiago Coelho Ferreto and Ricardo Mello and Rafael Pasquini and Marcos Schwarz and Leobino Nascimento Sampaio and Daniel Fernandes Macedo and José Ferreira Rezende and Kleber Vieira Cardoso and Flávio Oliveira Silva},
doi = {10.1109/ACCESS.2023.3292788},
year = {2023},
date = {2023-07-05},
journal = {IEEE Access},
volume = {11},
pages = {69144-69163},
abstract = {Network Slicing (NS) is an essential technique extensively used in 5G networks computing strategies, mobile edge computing, mobile cloud computing, and verticals like the Internet of Vehicles and industrial IoT, among others. NS is foreseen as one of the leading enablers for 6G futuristic and highly demanding applications since it allows the optimization and customization of scarce and disputed resources among dynamic, demanding clients with highly distinct application requirements. Various standardization organizations, like 3GPP’s proposal for new generation networks and state-of-the-art 5G/6G research projects, are proposing new NS architectures. However, new NS architectures have to deal with an extensive range of requirements that inherently result in having NS architecture proposals typically fulfilling the needs of specific sets of domains with commonalities. The Slicing Future Internet Infrastructures (SFI2) architecture proposal explores the gap resulting from the diversity of NS architectures target domains by proposing a new NS reference architecture with a defined focus on integrating experimental networks and enhancing the NS architecture with Machine Learning (ML) native optimizations, energy-efficient slicing, and slicing-tailored security functionalities. The SFI2 architectural main contribution includes the utilization of the slice-as-a-service paradigm for end-to-end orchestration of resources across multi-domains and multi-technology experimental networks. In addition, the SFI2 reference architecture instantiations will enhance the multi-domain and multi-technology integrated experimental network deployment with native ML optimization, energy-efficient aware slicing, and slicing-tailored security functionalities for the practical domain.},
keywords = {Experimental Approach, Machine Learning, Network Orchestration, Network Security, Network Virtualization & Slicing},
pubstate = {published},
tppubtype = {article}
}
Network Slicing (NS) is an essential technique extensively used in 5G networks computing strategies, mobile edge computing, mobile cloud computing, and verticals like the Internet of Vehicles and industrial IoT, among others. NS is foreseen as one of the leading enablers for 6G futuristic and highly demanding applications since it allows the optimization and customization of scarce and disputed resources among dynamic, demanding clients with highly distinct application requirements. Various standardization organizations, like 3GPP’s proposal for new generation networks and state-of-the-art 5G/6G research projects, are proposing new NS architectures. However, new NS architectures have to deal with an extensive range of requirements that inherently result in having NS architecture proposals typically fulfilling the needs of specific sets of domains with commonalities. The Slicing Future Internet Infrastructures (SFI2) architecture proposal explores the gap resulting from the diversity of NS architectures target domains by proposing a new NS reference architecture with a defined focus on integrating experimental networks and enhancing the NS architecture with Machine Learning (ML) native optimizations, energy-efficient slicing, and slicing-tailored security functionalities. The SFI2 architectural main contribution includes the utilization of the slice-as-a-service paradigm for end-to-end orchestration of resources across multi-domains and multi-technology experimental networks. In addition, the SFI2 reference architecture instantiations will enhance the multi-domain and multi-technology integrated experimental network deployment with native ML optimization, energy-efficient aware slicing, and slicing-tailored security functionalities for the practical domain.
2022
Fernando Ferreira Remde, Juliano Araujo Wickboldt
Analyzing Federated Learning Performance in Distributed Edge Scenarios Inproceedings
In: 27º Workshop de Gerência e Operação de Redes e Serviços, WGRS 2022, Virtual Conference, May 23-27, 2022, pp. 155-168, SBC, 2022, ISSN: 2595-2722.
Abstract Links BibTeX Tags: Federated Learning Fog & Edge Computing Machine Learning
@inproceedings{conf/wgrs/Remde22,
title = {Analyzing Federated Learning Performance in Distributed Edge Scenarios},
author = {Fernando Ferreira Remde and Juliano Araujo Wickboldt},
url = {https://www.inf.ufrgs.br/~jwickboldt/wp-content/uploads/Analyzing-Federated-Learning-Performance-in-Distributed-Edge-Scenarios-WGRS-2022-Camera-Ready.pdf
https://sol.sbc.org.br/index.php/wgrs/article/view/21484},
doi = {10.5753/wgrs.2022.223574},
issn = {2595-2722},
year = {2022},
date = {2022-05-23},
urldate = {2022-05-23},
booktitle = {27º Workshop de Gerência e Operação de Redes e Serviços, WGRS 2022, Virtual Conference, May 23-27, 2022},
pages = {155-168},
publisher = {SBC},
abstract = {Federated Learning is a machine learning paradigm where many clients cooperatively train a single centralized model while keeping their data private and decentralized. This novel paradigm imposes many challenges, such as dealing with data that is not independent and identically distributed, spread among multiple clients that are not synchronized and may have limited computing power. These clients are often edge devices such as smartphones and sensors, which form a system that is heterogeneous, highly distributed by nature and difficult to manage. This work proposes an architecture for running federated learning experiments in a distributed edge-like environment. Based on this architecture, a set of experiments are conducted to analyze how the overall system performance is affected by different configuration parameters and varied number of connected clients.},
keywords = {Federated Learning, Fog & Edge Computing, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Federated Learning is a machine learning paradigm where many clients cooperatively train a single centralized model while keeping their data private and decentralized. This novel paradigm imposes many challenges, such as dealing with data that is not independent and identically distributed, spread among multiple clients that are not synchronized and may have limited computing power. These clients are often edge devices such as smartphones and sensors, which form a system that is heterogeneous, highly distributed by nature and difficult to manage. This work proposes an architecture for running federated learning experiments in a distributed edge-like environment. Based on this architecture, a set of experiments are conducted to analyze how the overall system performance is affected by different configuration parameters and varied number of connected clients.