![]() Sample code, and illustrative experiments demonstrating system capabilities. Present an overview of Florida, including a description of the architecture, Service management from the FL workflow, enabling a cloud service provider toĭeliver FL-as-a-service (FLaaS) to ML engineers and application developers. Operating system (OS) and hardware specifications. Starting in Word, choose the Mailings menu, then Start Email Merge, and then Email Messages. Including C++, Java, and Python, enabling FL training across a wide range of Well as a multi-platform SDK supporting most major programming languages ![]() Simplify the task of deploying cross-device FL solutions by providingĬloud-hosted infrastructure and accompanying task management interfaces, as May have a wide variety of performance characteristics. Training process benefits from full participation of many client devices, which Scale and performance is a paramount concern, as the model Privacy and security mechanisms as well as scalable orchestration Deploying a FL solution requires implementation of complex Learning enables model training across devices and silos while the trainingĭata remains within its security boundary, by distributing a model snapshot toĪ client running inside the boundary, running client code to update the model,Īnd then aggregating updated snapshots across many clients in a central Whether on end-user devices or in segregated cloud storage silos. ![]() Privacy and security of data is best enabled by storing it at its origin, Machine learning based on a strong data sovereignty principle, i.e., that Kit (SDK) enabling deployment of large-scale Federated Learning (FL) solutionsĪcross a heterogeneous device ecosystem. Download a PDF of the paper titled Project Florida: Federated Learning Made Easy, by Daniel Madrigal Diaz and 4 other authors Download PDF Abstract: We present Project Florida, a system architecture and software development ![]()
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