Plankton-FL: Exploration of Federated Learning for Privacy-Preserving Training of Deep Neural Networks for Phytoplankton Classification

• URL: http://arxiv.org/abs/2212.08990v1
• Date: Sun, 18 Dec 2022 02:11:03 GMT
• Title: Plankton-FL: Exploration of Federated Learning for Privacy-Preserving Training of Deep Neural Networks for Phytoplankton Classification
• Authors: Daniel Zhang, Vikram Voleti, Alexander Wong and Jason Deglint
• Abstract summary: In this study, we explore the feasibility of leveraging federated learning for privacy-preserving training of deep neural networks for phytoplankton classification. We simulate two different federated learning frameworks, federated learning (FL) and mutually exclusive FL (ME-FL) Experimental results from this study demonstrate the feasibility and potential of federated learning for phytoplankton monitoring.
• Score: 81.04987357598802
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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