  This paper presents a federated learning (FL) approach to train an AI model
for SARS-Cov-2 variant classification. We analyze the SARS-CoV-2 spike
sequences in a distributed way, without data sharing, to detect different
variants of this rapidly mutating coronavirus. Our method maintains the
confidentiality of local data (that could be stored in different locations) yet
allows us to reliably detect and identify different known and unknown variants
of the novel coronavirus SARS-CoV-2. Using the proposed approach, we achieve an
overall accuracy of $93\%$ on the coronavirus variant identification task. We
also provide details regarding how the proposed model follows the main laws of
federated learning, such as Laws of data ownership, data privacy, model
aggregation, and model heterogeneity. Since the proposed model is distributed,
it could scale on ``Big Data'' easily. We plan to use this proof-of-concept to
implement a privacy-preserving pandemic response strategy.
