论文标题
基于区块链的监测，以分散的联邦学习中的毒攻击检测检测
Blockchain-based Monitoring for Poison Attack Detection in Decentralized Federated Learning
论文作者
论文摘要
联合学习（FL）是一种机器学习技术，它通过启用跨节点的模型培训，以当地数据集的访问权限来解决隐私挑战。为了实现分散的联合学习，提出了基于区块链的FL作为分布式FL架构。在分散的FL中，随着工人相互协作以训练全球模型，因此从学习过程中删除了酋长。分散的FL应用程序需要考虑基于区块链的FL部署所产生的额外延迟。尤其是在这种情况下，为了检测有针对性/无靶向的中毒攻击，我们研究了一个现实的分散FL过程的端到端学习完成延迟，以防止中毒攻击。我们提出了一项技术，该技术包括将监测阶段与检测阶段解耦，以防止在分散的联邦学习部署中防御中毒攻击，旨在监视工人的行为。我们证明了我们提出的基于区块链的监视提高了网络可伸缩性，鲁棒性和时间效率。操作的并行化导致在FL和区块链操作期间产生的端到端通信，计算和共识延迟的延迟最小化。
Federated Learning (FL) is a machine learning technique that addresses the privacy challenges in terms of access rights of local datasets by enabling the training of a model across nodes holding their data samples locally. To achieve decentralized federated learning, blockchain-based FL was proposed as a distributed FL architecture. In decentralized FL, the chief is eliminated from the learning process as workers collaborate between each other to train the global model. Decentralized FL applications need to account for the additional delay incurred by blockchain-based FL deployments. Particularly in this setting, to detect targeted/untargeted poisoning attacks, we investigate the end-to-end learning completion latency of a realistic decentralized FL process protected against poisoning attacks. We propose a technique which consists in decoupling the monitoring phase from the detection phase in defenses against poisoning attacks in a decentralized federated learning deployment that aim at monitoring the behavior of the workers. We demonstrate that our proposed blockchain-based monitoring improved network scalability, robustness and time efficiency. The parallelization of operations results in minimized latency over the end-to-end communication, computation, and consensus delays incurred during the FL and blockchain operations.
