Centralized vs Decentralized Federated Learning: A trade-off performance analysis

• URL: http://arxiv.org/abs/2605.16089v1
• Date: Fri, 15 May 2026 15:47:36 GMT
• Title: Centralized vs Decentralized Federated Learning: A trade-off performance analysis
• Abstract summary: Learning (FL) has emerged as a promising paradigm for collaborative model training across distributed edge devices.<n>FL can be Federated (CFL), Decentralized (DFL), and Semi-decentralized (SDFL)
• Score: 8.249351077204652
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) has emerged as a promising paradigm for collaborative model training across distributed edge devices while preserving data privacy especially with the huge increase amount of data due to the adoption of technologies which contributes to the growing number of IoT devices. Storing this amount of data centrally is challenging due to issues like limited communication, privacy, and regulations. FL can be Centralized (CFL), Decentralized (DFL), and Semi-decentralized (SDFL). Choosing the right FL architecture depends on the application's needs. However, very few research studies have experimentally compared these three types of architectures to not only understand the respective strengths and limitations, but also trade-offs between different performance indicators. This paper overcome this lack of analysis, conducting experimental analyses using the Fedstellar simulator, MNIST dataset, and MLP classifier.

Related papers

• Closer to Reality: Practical Semi-Supervised Federated Learning for Foundation Model Adaptation [56.36237936346563]
  Foundation models (FMs) exhibit remarkable generalization but require adaptation to downstream tasks.<n>Due to data privacy regulations, cloud-based FMs cannot directly access private edge data.<n>We introduce Practical Semi-Supervised Federated Learning (PSSFL), where edge devices hold only unlabeled, low-resolution data.<n>Our work paves the way for scalable and privacy-preserving FM adaptation in federated scenarios.
  arXiv  Detail & Related papers  (2025-08-22T17:47:02Z)
• Performance Analysis of Decentralized Federated Learning Deployments [1.7249361224827533]
  Decentralized Federated Learning (DFL) is introduced to address these challenges.<n>It facilitates direct collaboration among participating devices without relying on a central server.<n>This work explores crucial factors influencing the convergence and generalization capacity of DFL models.
  arXiv  Detail & Related papers  (2025-03-14T19:37:13Z)
• De-VertiFL: A Solution for Decentralized Vertical Federated Learning [7.877130417748362]
  This work introduces De-VertiFL, a novel solution for training models in a decentralized VFL setting.<n>De-VertiFL contributes by introducing a new network architecture distribution, an innovative knowledge exchange scheme, and a distributed federated training process.<n>The results demonstrate that De-VertiFL generally surpasses state-of-the-art methods in F1-score performance, while maintaining a decentralized and privacy-preserving framework.
  arXiv  Detail & Related papers  (2024-10-08T15:31:10Z)
• Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework [70.83511997272457]
  We propose a semi-federated learning (SemiFL) paradigm to leverage both the base station (BS) and devices for a hybrid implementation of centralized learning (CL) and FL. We propose a two-stage algorithm to solve this intractable problem, in which we provide the closed-form solutions to the beamformers.
  arXiv  Detail & Related papers  (2023-10-04T03:32:39Z)
• Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges [0.0]
  Federated Learning (FL) has gained relevance in training collaborative models without sharing sensitive data. Decentralized Federated Learning (DFL) emerged to address these concerns by promoting decentralized model aggregation. This article identifies and analyzes the main fundamentals of DFL in terms of federation architectures, topologies, communication mechanisms, security approaches, and key performance indicators.
  arXiv  Detail & Related papers  (2022-11-15T18:51:20Z)
• Federated Learning and Meta Learning: Approaches, Applications, and Directions [94.68423258028285]
  In this tutorial, we present a comprehensive review of FL, meta learning, and federated meta learning (FedMeta) Unlike other tutorial papers, our objective is to explore how FL, meta learning, and FedMeta methodologies can be designed, optimized, and evolved, and their applications over wireless networks.
  arXiv  Detail & Related papers  (2022-10-24T10:59:29Z)
• Online Data Selection for Federated Learning with Limited Storage [53.46789303416799]
  Federated Learning (FL) has been proposed to achieve distributed machine learning among networked devices. The impact of on-device storage on the performance of FL is still not explored. In this work, we take the first step to consider the online data selection for FL with limited on-device storage.
  arXiv  Detail & Related papers  (2022-09-01T03:27:33Z)
• A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learning [48.63610479916003]
  This article breaks down and analyzes the main factors that influence the environmental footprint of distributed learning policies. It models both vanilla and decentralized FL policies driven by consensus. Results show that FL allows remarkable end-to-end energy savings (30%-40%) for wireless systems characterized by low bit/Joule efficiency.
  arXiv  Detail & Related papers  (2021-03-18T16:04:42Z)
• Federated Learning for Physical Layer Design [38.46522285374866]
  Federated learning (FL) has been proposed recently as a distributed learning scheme. FL is more communication-efficient and privacy-preserving than centralized learning (CL) This article discusses the recent advances in FL-based training for physical layer design problems.
  arXiv  Detail & Related papers  (2021-02-23T16:22:53Z)
• FedML: A Research Library and Benchmark for Federated Machine Learning [55.09054608875831]
  Federated learning (FL) is a rapidly growing research field in machine learning. Existing FL libraries cannot adequately support diverse algorithmic development. We introduce FedML, an open research library and benchmark to facilitate FL algorithm development and fair performance comparison.
  arXiv  Detail & Related papers  (2020-07-27T13:02:08Z)
• Wireless Communications for Collaborative Federated Learning [160.82696473996566]
  Internet of Things (IoT) devices may not be able to transmit their collected data to a central controller for training machine learning models. Google's seminal FL algorithm requires all devices to be directly connected with a central controller. This paper introduces a novel FL framework, called collaborative FL (CFL), which enables edge devices to implement FL with less reliance on a central controller.
  arXiv  Detail & Related papers  (2020-06-03T20:00:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.