【Author】
Ilias, Chamatidis; Georgios, Spathoulas
【Source】PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS SECURITY AND PRIVACY (ICISSP)
【Abstract】Machine learning and especially deep learning are appropriate for solving multiple problems in various domains. Training such models though, demands significant processing power and requires large data-sets. Federated learning is an approach that merely solves these problems, as multiple users constitute a distributed network and each one of them trains a model locally with his data. This network can cumulatively sum up significant processing power to conduct training efficiently, while it is easier to preserve privacy, as data does not leave its owner. Nevertheless, it has been proven that federated learning also faces privacy and integrity issues. In this paper a general enhanced federated learning framework is presented. Users may provide data or the required processing power or participate just in order to train their models. Homomorphic encryption algorithms are employed to enable model training on encrypted data. Blockchain technology is used as smart contracts coordinate the work-flow and the commitments made between all participating nodes, while at the same time, tokens exchanges between nodes provide the required incentives for users to participate in the scheme and to act legitimately.
【Keywords】Deep Learning; Federated Learning; Blockchain; Security; Privacy; Integrity; Incentives
【标题】面向所有人的机器学习：更强大的联邦学习框架
【摘要】机器学习，尤其是深度学习，适用于解决各个领域的多个问题。但是，训练这样的模型需要强大的处理能力并且需要大量的数据集。联邦学习是一种仅仅解决这些问题的方法，因为多个用户构成了一个分布式网络，每个用户都使用他的数据在本地训练一个模型。该网络可以累积强大的处理能力以有效地进行培训，同时更容易保护隐私，因为数据不会离开其所有者。然而，事实证明，联邦学习也面临隐私和完整性问题。在本文中，提出了一个通用的增强型联邦学习框架。用户可以提供数据或所需的处理能力，或者只是为了训练他们的模型而参与。采用同态加密算法来实现对加密数据的模型训练。区块链技术被用作智能合约协调所有参与节点之间的工作流程和承诺，同时，节点之间的代币交换为用户参与计划和合法行动提供了必要的激励。
【关键词】深度学习；联邦学习；区块链；安全;隐私;正直;激励措施
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