  TinyML has rose to popularity in an era where data is everywhere. However,
the data that is in most demand is subject to strict privacy and security
guarantees. In addition, the deployment of TinyML hardware in the real world
has significant memory and communication constraints that traditional ML fails
to address. In light of these challenges, we present TinyFedTL, the first
implementation of federated transfer learning on a resource-constrained
microcontroller.
