Centralized vs Decentralized Federated Learning: A trade-off performance analysis
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 30 references that are in the map. First posted on arXiv 15 May 2026.
Builds on
17 of 30 references.
References · 17
- 2019Advances and Open Problems in Federated Learningcited by 3,046
- 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
- 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
- 2019Towards Federated Learning at Scale: System Designcited by 832
- 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
- 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 252
- 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 139
- 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 138
- 2019BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learningcited by 117
- 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
- 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
- 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 29
5 more
- 2022Semi-Decentralized Federated Learning with Collaborative Relayingcited by 16
- 2022Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Reviewcited by 15
- 2023Fedstellar: A Platform for Decentralized Federated Learningcited by 14
- 2018Taming the Cross Entropy Losscited by 10
- 2022A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehiclescited by 7
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Citing papers per year: 2026: 1.
1 paper.