TY - GEN
T1 - BlockLearning: A Modular Framework for Blockchain-Based Vertical Federated Learning
AU - Coelho Dias, Henrique A.
AU - Meratnia, Nirvana
PY - 2023/2/16
Y1 - 2023/2/16
N2 - Federated Learning allows multiple distributed clients to collaborate on training the same Machine Learning model. Blockchain-based Federated Learning has emerged in recent years to improve its transparency, traceability, auditability, authentication, persistency, and information safety. Various Blockchain-based Horizontal Federated Learning models are to be found in the literature. However, to the best of our knowledge, no solution for Blockchain-based Vertical Federated Learning exists. In this paper, we introduce BlockLearning, an extensible and modular framework that supports Vertical Federated Learning and different types of blockchain related algorithms. We also present performance evaluation results in terms of execution time, transaction cost, transaction latency, model accuracy and convergence, as well as communication and computation costs when BlockLearning is applied to vertically partitioned data.
AB - Federated Learning allows multiple distributed clients to collaborate on training the same Machine Learning model. Blockchain-based Federated Learning has emerged in recent years to improve its transparency, traceability, auditability, authentication, persistency, and information safety. Various Blockchain-based Horizontal Federated Learning models are to be found in the literature. However, to the best of our knowledge, no solution for Blockchain-based Vertical Federated Learning exists. In this paper, we introduce BlockLearning, an extensible and modular framework that supports Vertical Federated Learning and different types of blockchain related algorithms. We also present performance evaluation results in terms of execution time, transaction cost, transaction latency, model accuracy and convergence, as well as communication and computation costs when BlockLearning is applied to vertically partitioned data.
KW - Blockchain
KW - Blockchain-based federated learning
KW - Horizontal federated learning
KW - Vertical federated learning
UR - http://www.scopus.com/inward/record.url?scp=85151047441&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-0272-9_22
DO - 10.1007/978-981-99-0272-9_22
M3 - Conference contribution
SN - 978-981-99-0271-2
T3 - Communications in Computer and Information Science
SP - 319
EP - 333
BT - The Second International Conference on Ubiquitous Security, UbiSec 2022
A2 - Wang, Guojun
A2 - Choo, Kim-Kwang Raymond
A2 - Wu, Jie
A2 - Damiani, Ernesto
PB - Springer
CY - Singapore
T2 - The Second International Conference on Ubiquitous Security
Y2 - 28 December 2022 through 31 December 2022
ER -