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Creating high-performance generalizable deep neural networks forphytoplankton monitoring requires utilizing large-scale data coming fromdiverse global water sources. A major challenge to training such networks liesin data privacy, where data collected at different facilities are oftenrestricted from being transferred to a centralized location. A promisingapproach to overcome this challenge is federated learning, where training isdone at site level on local data, and only the model parameters are exchangedover the network to generate a global model. In this study, we explore thefeasibility of leveraging federated learning for privacy-preserving training ofdeep neural networks for phytoplankton classification. More specifically, wesimulate two different federated learning frameworks, federated learning (FL)and mutually exclusive FL (ME-FL), and compare their performance to atraditional centralized learning (CL) framework. Experimental results from thisstudy demonstrate the feasibility and potential of federated learning forphytoplankton monitoring.

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