Project Florida: Federated Learning Made Easy

We present Project Florida, a system architecture and software development kit (SDK) enabling
deployment of large-scale Federated Learning (FL) solutions across a heterogeneous device ecosystem.
Federated learning is an approach to machine learning based on a strong data sovereignty principle- i.e.
that privacy and security of data is best enabled by storing it at its origin, whether on end-user devices
or in segregated cloud storage silos. Federated learning enables model training across devices and silos
while the training data remains within its security boundary, by distributing a model snapshot to a client
running inside the boundary, running client code to update the model, and then aggregating updated
snapshots across many clients in a central orchestrator. Deploying a FL solution requires implementation
of complex privacy and security mechanisms as well as scalable orchestration infrastructure. Scale and
performance is a paramount concern, as the model training process benefits from full participation of
many client devices, which may have a wide variety of performance characteristics. Project Florida
aims to simplify the task of deploying cross-device FL solutions by providing cloud-hosted infrastructure
and accompanying task management interfaces, as well as a multi-platform SDK supporting most major
programming languages including C++, Java, and Python, enabling FL training across a wide range of
operating system (OS) and hardware specifications. The architecture decouples service management from
the FL workflow, enabling a cloud service provider to deliver FL-as-a-service (FLaaS) to ML engineers
and application developers. We present an overview of Florida, including a description of the architecture,
sample code, and illustrative experiments demonstrating system capabilities.