Federated Learning using Peer-to-peer Network for Decentralized Orchestration of Model Weights
Federated Learning using Peer-to-peer Network for Decentralized Orchestration of Model Weights
复制标题
使用点对点网络的联邦学习来分散编排模型权重
DOI：
10.36227/techrxiv.14267468.v1
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
R. Otter
中科院分区：
文献类型：
--
作者：
Monik R Behera;Sudhir Upadhyay;S. Shetty;R. Otter
近年来，机器学习和人工智能已成为计算机科学的重要新兴领域之一。许多研究人员和企业受益于通过大规模数据处理训练的机器学习模型。然而，机器学习，特别是深度学习需要大量的数据，在某些情况下，这些数据是许多企业的专有和机密。为了在协作机器学习中尊重个体组织的隐私，联合学习可以起到至关重要的作用。这种保护隐私的联合学习的实现在金融、医疗保健、法律、研究和其他需要保护隐私的领域中都有适用性。然而，许多这样的实施是由网络中的集中式体系结构驱动的，其中聚合器节点成为单点故障，并且预计还会有大量计算资源可供其使用。在本文中，我们提出了一种实现分散的、对等的联邦学习框架的方法，该框架利用基于RAFT的聚集器选择。该提议基于这样一个事实，即没有一个永久的聚集器，而是一个短暂的、基于时间的选举领导人，它将聚合网络中所有对等点的模型。领导者(聚合器)将聚合模型发布在网络上，供每个人消费。与点对点网络和基于RAFT的聚合器选择一起，该框架使用动态生成密钥，以创建更安全的机制来在网络中交付模型。密钥轮换还确保了网络上发送者的匿名性。通过实验，验证了对等网络在构建弹性联邦学习网络中的作用。尽管所提出的解决方案在其参考实现中使用了人工神经网络，但该框架的通用设计可以适应网络中的任何联邦学习模型。
In recent times, Machine learning and Artificial intelligence have become one of the key emerging fields of computer science. Many researchers and businesses are benefited by machine learning models that are trained by data processing at scale. However, machine learning, and particularly Deep Learning requires large amounts of data, that in several instances are proprietary and confidential to many businesses. In order to respect individual organization’s privacy in collaborative machine learning, federated learning could play a crucial role. Such implementations of privacy preserving federated learning find applicability in various ecosystems like finance, health care, legal, research and other fields that require preservation of privacy. However, many such implementations are driven by a centralized architecture in the network, where the aggregator node becomes the single point of failure, and is also expected with lots of computing resources at its disposal. In this paper, we propose an approach of implementing a decentralized, peer-topeer federated learning framework, that leverages RAFT based aggregator selection. The proposal hinges on that fact that there is no one permanent aggregator, but instead a transient, time based elected leader, which will aggregate the models from all the peers in the network. The leader ( aggregator) publishes the aggregated model on the network, for everyone to consume. Along with peer-to-peer network and RAFT based aggregator selection, the framework uses dynamic generation of cryptographic keys, to create a more secure mechanism for delivery of models within the network. The key rotation also ensures anonymity of the sender on the network too. Experiments conducted in the paper, verifies the usage of peer-to-peer network for creating a resilient federated learning network. Although the proposed solution uses an artificial neural network in it’s reference implementation, the generic design of the framework can accommodate any federated learning model within the network.
DOI：
10.1002/jsc.2148
发表时间：
2017
期刊：
Strategic Change
影响因子：
--
作者：
Maull R
通讯作者：
Maull R