Blockchain-based Approaches for Secure Federated Learning
In federated learning, different clients can contribute to the learning process without disclosing private information. Nevertheless, other security issues do still exist. Malicious users can delay the convergence or even degrade the final global model by injecting untrained models. This paper integrates blockchain technology with federated learning and evaluates the performance based on small committees. Based on real experiments, we show that registering all model updates in blockchain introduces system auditability without impacting the federated learning performance.
Authors
- Gabriel Antonio F. Rebello (ORCID: https://orcid.org/0000-0003-3344-0734)
- Lucas C. B. Guimarães (ORCID: https://orcid.org/0000-0003-4664-4912)
- Luís Henrique M. K. Costa (ORCID: https://orcid.org/0000-0002-7789-3359)
- Gustavo F. Camilo (ORCID: https://orcid.org/0000-0002-8164-8567)
- Miguel Elias M. Campista (ORCID: https://orcid.org/0000-0002-8752-9382)
- Lucas Airam C. de Souza (ORCID: https://orcid.org/0000-0002-8941-3121)
Institutions
- Universidade Federal do Rio de Janeiro (BR)
Publication Details
- Published
- 2024-10-09
- DOI
- https://doi.org/10.1109/brains63024.2024.10732504
- Citations
- 3
- Primary Topic
- Privacy-Preserving Technologies in Data
- Type
- article
- Field-Weighted Citation Impact
- 1.04
Funders
- Fundação de Amparo à Pesquisa do Estado de São Paulo
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior