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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Fedstellar: A Platform for Training Models in a Privacy-preserving and Decentralized Fashion |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | No |
| Status | Published in final form |
| Language |
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| ISBN | 978-1-956792-03-4 |
| Page Range | 7154 - 7157 |
| Event Title | Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23} |
| Event Type | conference |
| Event Location | Macau, SAR China |
| Event Start Date | August 19 - 2023 |
| Event End Date | August 25 - 2023 |
| Series Name | Proceedings of the International Joint Conference on Artificial Intelligence |
| Publisher | IJCAI |
| Abstract Text | This paper presents Fedstellar, a platform for training decentralized Federated Learning (FL) models in heterogeneous topologies in terms of the number of federation participants and their connections. Fedstellar allows users to build custom topologies, enabling them to control the aggregation of model parameters in a decentralized manner. The platform offers a Web application for creating, managing, and connecting nodes to ensure data privacy and provides tools to measure, monitor, and analyze the performance of the nodes. The paper describes the functionalities of Fedstellar and its potential applications. To demonstrate the applicability of the platform, different use cases are presented in which decentralized, semi-decentralized, and centralized architectures are compared in terms of model performance, convergence time, and network overhead when collaboratively classifying hand-written digits using the MNIST dataset. |
| Digital Object Identifier | 10.24963/ijcai.2023/838 |
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