ST-BFL: A Structured Transparency Empowered Cross-Silo Federated Learning on the Blockchain Framework

Federated Learning (FL) relies on on-device training to avoid the migration of devices’ data to a centralized server to address privacy leakage. Moreover, FL is feasible for scenarios (e.g., autonomous cars) where an enormous amount of data is generated every day. Transferring only local...

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Autores principales:	Umer Majeed, Latif U. Khan, Abdullah Yousafzai, Zhu Han, Bang Ju Park, Choong Seon Hong
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Blockchain Ethereum federated learning flow governance homomorphic encryption input privacy Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/46f427ed361841c381889c306c59b45a
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id	oai:doaj.org-article:46f427ed361841c381889c306c59b45a
record_format	dspace
spelling	oai:doaj.org-article:46f427ed361841c381889c306c59b45a2021-11-26T00:01:46ZST-BFL: A Structured Transparency Empowered Cross-Silo Federated Learning on the Blockchain Framework2169-353610.1109/ACCESS.2021.3128622https://doaj.org/article/46f427ed361841c381889c306c59b45a2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9617624/https://doaj.org/toc/2169-3536Federated Learning (FL) relies on on-device training to avoid the migration of devices’ data to a centralized server to address privacy leakage. Moreover, FL is feasible for scenarios (e.g., autonomous cars) where an enormous amount of data is generated every day. Transferring only local model updates in the case of FL is highly communication-efficient compared to transferring all data in the case of centralized machine learning (ML). Although FL offers many advantages, it also has some challenges. A malicious aggregation server can infer device information via local model updates. Another downside of FL is the centralized aggregation server that can malfunction due to an attack or physical damage. To address these issues, we propose a novel Structured Transparency empowered cross-silo Federated Learning on the Blockchain (ST-BFL) framework. In ST-BFL, homomorphic encryption, FL-aggregators, FL-verifiers, and smart contract are employed, which satisfy various structured transparency components, such as input privacy, output privacy, output verification, and flow governance. We present the framework architecture, algorithms, and sequence diagram of our ST-BFL framework to show how different entities interact in ST-BFL for the FL process. We also present a simplified class diagram of ST-BFL’s smart contract for an FL task. Finally, we perform a simulation to analyze our framework from the perspective of aggregation time, accuracy, and storage size. The qualitative and quantitative evaluation shows that ST-BFL has the same accuracy as traditional FL. However, ST-BFL provides input privacy, output privacy, input verification, output verification, and flow governance at the expense of relatively higher computation and communication costs than traditional FL.Umer MajeedLatif U. KhanAbdullah YousafzaiZhu HanBang Ju ParkChoong Seon HongIEEEarticleBlockchainEthereumfederated learningflow governancehomomorphic encryptioninput privacyElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 155634-155650 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Blockchain Ethereum federated learning flow governance homomorphic encryption input privacy Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Blockchain Ethereum federated learning flow governance homomorphic encryption input privacy Electrical engineering. Electronics. Nuclear engineering TK1-9971 Umer Majeed Latif U. Khan Abdullah Yousafzai Zhu Han Bang Ju Park Choong Seon Hong ST-BFL: A Structured Transparency Empowered Cross-Silo Federated Learning on the Blockchain Framework
description	Federated Learning (FL) relies on on-device training to avoid the migration of devices’ data to a centralized server to address privacy leakage. Moreover, FL is feasible for scenarios (e.g., autonomous cars) where an enormous amount of data is generated every day. Transferring only local model updates in the case of FL is highly communication-efficient compared to transferring all data in the case of centralized machine learning (ML). Although FL offers many advantages, it also has some challenges. A malicious aggregation server can infer device information via local model updates. Another downside of FL is the centralized aggregation server that can malfunction due to an attack or physical damage. To address these issues, we propose a novel Structured Transparency empowered cross-silo Federated Learning on the Blockchain (ST-BFL) framework. In ST-BFL, homomorphic encryption, FL-aggregators, FL-verifiers, and smart contract are employed, which satisfy various structured transparency components, such as input privacy, output privacy, output verification, and flow governance. We present the framework architecture, algorithms, and sequence diagram of our ST-BFL framework to show how different entities interact in ST-BFL for the FL process. We also present a simplified class diagram of ST-BFL’s smart contract for an FL task. Finally, we perform a simulation to analyze our framework from the perspective of aggregation time, accuracy, and storage size. The qualitative and quantitative evaluation shows that ST-BFL has the same accuracy as traditional FL. However, ST-BFL provides input privacy, output privacy, input verification, output verification, and flow governance at the expense of relatively higher computation and communication costs than traditional FL.
format	article
author	Umer Majeed Latif U. Khan Abdullah Yousafzai Zhu Han Bang Ju Park Choong Seon Hong
author_facet	Umer Majeed Latif U. Khan Abdullah Yousafzai Zhu Han Bang Ju Park Choong Seon Hong
author_sort	Umer Majeed
title	ST-BFL: A Structured Transparency Empowered Cross-Silo Federated Learning on the Blockchain Framework
title_short	ST-BFL: A Structured Transparency Empowered Cross-Silo Federated Learning on the Blockchain Framework
title_full	ST-BFL: A Structured Transparency Empowered Cross-Silo Federated Learning on the Blockchain Framework
title_fullStr	ST-BFL: A Structured Transparency Empowered Cross-Silo Federated Learning on the Blockchain Framework
title_full_unstemmed	ST-BFL: A Structured Transparency Empowered Cross-Silo Federated Learning on the Blockchain Framework
title_sort	st-bfl: a structured transparency empowered cross-silo federated learning on the blockchain framework
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/46f427ed361841c381889c306c59b45a
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ST-BFL: A Structured Transparency Empowered Cross-Silo Federated Learning on the Blockchain Framework

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