A Blockchained Incentive Architecture for Federated Learning

Autor(es)

de Brito Goncalves, Joao Paulo ; Villaca, Rodolfo da Silva ; IEEE Comp Soc

• Tipo de documento: Artigo Científico
• Fonte: 2022 IEEE INTERNATIONAL CONFERENCE ON BLOCKCHAIN (BLOCKCHAIN 2022); v. N/A, p. 6-pg., 2022-01-01.

Resumo

The naive use of Federated Learning (FL) in a distributed environment exposes it to a risk of corruption, whether intentional or not, during the training phase. It happens because of the lack of monitoring of the training increments and difficulty of checking the quality of the training datasets. A very common type of attack of this type is Model Poisoning. To improve the security of the FL structure, we propose a decentralized FL framework based on blockchain, that is, a blockchain-based FL framework to increment the system security using an incentive mechanism to reward good trainers in the form of tokens. The system modeling will be presented as well as its implementation in the Mininet simulator. The validation tests performed to attest its accuracy were executed using the MNIST dataset. (AU)

• Processo FAPESP: 20/05182-3 - PORVIR-5G: programabilidade, orquestração e virtualização em redes 5G
• Beneficiário: José Marcos Silva Nogueira
• Modalidade de apoio: Auxílio à Pesquisa - Temático