This paper explores integrating federated learning methodologies to optimize and generalize sensor design for plasmonic-based fiber optic sensors (FOS) applicable in biosensing, removing reliance on specific experimental datasets. By employing machine learning (ML) models, the enhancement of FOS design’s figure of merit (FOM) becomes achievable through training on localized data. However, to establish a more universally applicable ML model tailored to distinct applications, amalgamating data and training from diverse sources becomes imperative. FOS finds extensive utility in medical contexts where data privacy stands as a paramount concern, necessitating stringent consent and regulatory adherence for data sharing. Given the challenges posed by decentralized datasets and the criticality of data privacy, federated learning emerges as an indispensable framework, enabling the refinement of generalized ML models while upholding the sanctity of individual data privacy. Through the utilization of the Gaussian Process Regressor (GPR) model for localized training within discrete datasets, federated learning facilitates collaborative model refinement without compromising data privacy. This collaborative approach harnesses collective insights to bolster sensor performance, preserving data privacy boundaries and demonstrating potential enhancements without centralizing data aggregation. The research underscores the potential of federated learning in optimizing sensor design, accentuating its pivotal role in elevating sensing proficiency while safeguarding individual data privacy constraints, thereby paving the way for forthcoming practical implementations in biosensing applications.