Evaluating Federated Learning Scenarios: Impact of Basic Parameters and Realistic Communication in Testbeds

This paper evaluates Federated Learning in different scenarios to identify differences between simulations and hardware. The scenarios include the MENTORED Testbed with x86 nodes, an experimental cluster with aarch64 architectures, and a server simulating FL using Docker.

Authors

Aurora Pozo (ORCID: https://orcid.org/0000-0001-5808-3919)
Bruno H. Meyer (ORCID: https://orcid.org/0000-0001-6747-1155)
Michele Nogueira (ORCID: https://orcid.org/0000-0001-5427-2384)
Wagner M. Nunan Zola (ORCID: https://orcid.org/0000-0002-2282-0398)

Publication Details

Published: 2025-04-23
DOI: https://doi.org/10.5753/eradrs.2025.6837
Primary Topic: Privacy-Preserving Technologies in Data
Type: article
Field-Weighted Citation Impact: 0.00

Controls

|||

ALL TIME

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

article

Evaluating Federated Learning Scenarios: Impact of Basic Parameters and Realistic Communication in Testbeds

Aurora Pozo, Bruno H. Meyer, Michele Nogueira, Wagner M. Nunan Zola

Apr 23, 2025

Privacy-Preserving Technologies in Data

article

Evaluating Federated Learning Scenarios: Impact of Basic Parameters and Realistic Communication in Testbeds

Aurora Pozo, Bruno H. Meyer, Michele Nogueira, Wagner M. Nunan Zola

April 23, 2025article en

Abstract

This paper evaluates Federated Learning in different scenarios to identify differences between simulations and hardware. The scenarios include the MENTORED Testbed with x86 nodes, an experimental cluster with aarch64 architectures, and a server simulating FL using Docker.

Openalex Percentile: Top 94%

Privacy-Preserving Technologies in Data

DOI Open Access PDF OpenAlex

AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Evaluating Federated Learning Scenarios: Impact of Basic Parameters and Realistic Communication in Testbeds — Aurora Pozo, Bruno H. Meyer, et al. · (2025) | TGRS Research Map | TGRS