Project Florida: Federated Learning Made Easy
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 19 of its 32 references that are in the map. First posted on arXiv 21 Jul 2023.
Builds on
19 of 32 references.
References · 19
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- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2018Federated Optimization in Heterogeneous Networkscited by 2,971
- 2016Deep Learning with Differential Privacycited by 2,682
- 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,201
- 2019Towards Federated Learning at Scale: System Designcited by 826
- 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
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- 2018Subsampled R'enyi Differential Privacy and Analytical Moments Accountantcited by 234
- 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
- 2021Papaya: Practical, Private, and Scalable Federated Learningcited by 89
7 more
- 2021Federated Evaluation and Tuning for On-Device Personalization: System Design & Applicationscited by 84
- 2022NVIDIA FLARE: Federated Learning from Simulation to Real-Worldcited by 78
- 2022FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulationscited by 27
- 2020Pretraining Federated Text Models for Next Word Predictioncited by 22
- 2021Federated Learning of User Verification Models Without Sharing Embeddingscited by 11
- 2022Learnings from Federated Learning in the Real worldcited by 6
- 2021Dynamic Gradient Aggregation for Federated Domain Adaptationcited by 4
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.