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

【Author】 Majeed, Umer; Khan, Latif U.; Yousafzai, Abdullah; Han, Zhu; Park, Bang Ju; Hong, Choong Seon

【Source】IEEE ACCESS

【Abstract】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.

【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 transparenc

【标题】ST-BFL：基于区块链框架的结构化透明授权cross-silo联邦学习

【摘要】联邦学习 (FL) 依靠设备上的训练来避免将设备数据迁移到集中式服务器以解决隐私泄露问题。此外，FL 对于每天产生大量数据的场景（例如自动驾驶汽车）是可行的。与在集中式机器学习 (ML) 的情况下传输所有数据相比，在 FL 的情况下仅传输本地模型更新具有很高的通信效率。尽管 FL 提供了许多优点，但它也存在一些挑战。恶意聚合服务器可以通过本地模型更新推断设备信息。 FL 的另一个缺点是集中式聚合服务器可能由于攻击或物理损坏而发生故障。为了解决这些问题，我们提出了一种新颖的结构化透明授权跨筒仓联邦学习区块链（ST-BFL）框架。在 ST-BFL 中，采用同态加密、FL 聚合器、FL 验证器和智能合约，满足各种结构化透明组件，例如输入隐私、输出隐私、输出验证和流治理。我们展示了我们的 ST-BFL 框架的框架架构、算法和序列图，以展示不同实体如何在 ST-BFL 中为 FL 过程进行交互。我们还展示了 ST-BFL 用于 FL 任务的智能合约的简化类图。最后，我们进行模拟，从聚合时间、准确性和存储大小的角度分析我们的框架。定性和定量评估表明，ST-BFL 与传统 FL 具有相同的准确性。然而，ST-BFL 提供了输入隐私、输出隐私、输入验证、输出验证和流治理，但代价是比传统 FL 相对更高的计算和通信成本。

【关键词】智能合约；隐私;服务器；计算建模；区块链；协作工作；任务分析；区块链；以太坊；联邦学习；流量治理；同态加密；输入隐私；输入验证；输出隐私；输出验证；智能合约；结构化的透明度

【发表时间】2021

【收录时间】2022-07-06

【文献类型】Article

【论文大主题】区块链联邦学习

【论文小主题】联邦学习为主体

【影响因子】3.476

【翻译者】石东瑛