  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.
