We present Project Florida, a system architecture and software development kit (SDK) enabling
\ndeployment of large-scale Federated Learning (FL) solutions across a heterogeneous device ecosystem.
\nFederated learning is an approach to machine learning based on a strong data sovereignty principle- i.e.
\nthat privacy and security of data is best enabled by storing it at its origin, whether on end-user devices
\nor in segregated cloud storage silos. Federated learning enables model training across devices and silos
\nwhile the training data remains within its security boundary, by distributing a model snapshot to a client
\nrunning inside the boundary, running client code to update the model, and then aggregating updated
\nsnapshots across many clients in a central orchestrator. Deploying a FL solution requires implementation
\nof complex privacy and security mechanisms as well as scalable orchestration infrastructure. Scale and
\nperformance is a paramount concern, as the model training process benefits from full participation of
\nmany client devices, which may have a wide variety of performance characteristics. Project Florida
\naims to simplify the task of deploying cross-device FL solutions by providing cloud-hosted infrastructure
\nand accompanying task management interfaces, as well as a multi-platform SDK supporting most major
\nprogramming languages including C++, Java, and Python, enabling FL training across a wide range of
\noperating system (OS) and hardware specifications. The architecture decouples service management from
\nthe FL workflow, enabling a cloud service provider to deliver FL-as-a-service (FLaaS) to ML engineers
\nand application developers. We present an overview of Florida, including a description of the architecture,
\nsample code, and illustrative experiments demonstrating system capabilities.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 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