论文标题
Plankton-FL：探索联合学习，用于保护浮游植物分类的深度神经网络的隐私培训
Plankton-FL: Exploration of Federated Learning for Privacy-Preserving Training of Deep Neural Networks for Phytoplankton Classification
论文作者
论文摘要
为浮游植物监测创建高性能的深层神经网络，需要利用来自各种全球水源的大规模数据。培训此类网络的一个主要挑战在于数据隐私，在不同的设施中收集的数据通常受到转移到集中位置的限制。克服这一挑战的一种有希望的方法是联合学习，在该学习上，在本地数据上进行现场培训，并且只有在网络上交换模型参数以生成全局模型。在这项研究中，我们探讨了利用联邦学习进行隐私神经网络进行隐私培训进行浮游植物分类的可行性。更具体地说，我们模拟了两个不同的联合学习框架，即联邦学习（FL）和互斥的FL（ME-FL），并将其绩效与传统的集中学习（CL）框架进行比较。这项研究的实验结果表明，联邦学习对浮游植物监测的可行性和潜力。
Creating high-performance generalizable deep neural networks for phytoplankton monitoring requires utilizing large-scale data coming from diverse global water sources. A major challenge to training such networks lies in data privacy, where data collected at different facilities are often restricted from being transferred to a centralized location. A promising approach to overcome this challenge is federated learning, where training is done at site level on local data, and only the model parameters are exchanged over the network to generate a global model. In this study, we explore the feasibility of leveraging federated learning for privacy-preserving training of deep neural networks for phytoplankton classification. More specifically, we simulate two different federated learning frameworks, federated learning (FL) and mutually exclusive FL (ME-FL), and compare their performance to a traditional centralized learning (CL) framework. Experimental results from this study demonstrate the feasibility and potential of federated learning for phytoplankton monitoring.
