Centralized vs Decentralized Federated Learning: A trade-off performance analysis
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866916016216342528 |
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| author | Medjadji, Chaimaa Leduc, Guilain Kubler, Sylvain Traon, Yves Le |
| author_facet | Medjadji, Chaimaa Leduc, Guilain Kubler, Sylvain Traon, Yves Le |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_16089 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Centralized vs Decentralized Federated Learning: A trade-off performance analysis Medjadji, Chaimaa Leduc, Guilain Kubler, Sylvain Traon, Yves Le Machine Learning Artificial Intelligence 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. |
| title | Centralized vs Decentralized Federated Learning: A trade-off performance analysis |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2605.16089 |