Journal
IEEE ACCESS
Volume 9, Issue -, Pages 155634-155650Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3128622
Keywords
Smart contracts; Privacy; Servers; Computational modeling; Blockchains; Collaborative work; Task analysis; Blockchain; Ethereum; federated learning; flow governance; homomorphic encryption; input privacy; input verification; output privacy; output verification; smart contract; structured transparency
Categories
Funding
- Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant by the Korean Government through the MSIT (Evolvable Deep Learning Model Generation Platform for Edge Computing) [2019-0-01287]
- Korea Institute of Energy Technology Evaluation and Planning (KETEP)
- Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea [20209810400030]
- Korea Evaluation Institute of Industrial Technology (KEIT) [20209810400030] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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The translation discusses the advantages and challenges of Federated Learning, as well as the proposed solution of the ST-BFL framework. The framework provides additional privacy and verification protection while maintaining the same accuracy as traditional FL, but at the cost of higher computation and communication costs.
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.
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