Poster
in
Workshop: Federated Learning: Recent Advances and New Challenges

FLARE: Federated Learning from Simulation to Real-World

Holger Roth ⋅ Yan Cheng ⋅ Yuhong Wen ⋅ Te-Chung (Isaac) Yang ⋅ Ziyue Xu ⋅ Yuan-Ting Hsieh ⋅ Kristopher Kersten ⋅ Ahmed Harouni ⋅ Can Zhao ⋅ Kevin Lu ⋅ Zhihong Zhang ⋅ Wenqi Li ⋅ Andriy Myronenko ⋅ Dong Yang ⋅ Sean Yang ⋅ Nicola Rieke ⋅ Abood Quraini ⋅ Chester Chen ⋅ Daguang Xu ⋅ Nic Ma ⋅ Prerna Dogra ⋅ Mona Flores ⋅ Andrew Feng

Project Page [ Poster] [ OpenReview]

Abstract

Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data. We created FLARE as an open-source software development kit (SDK) to make it easier for data scientists to use FL in their research and real-world applications. The SDK includes solutions for state-of-the-art FL algorithms and federated machine learning approaches, which facilitate building workflows for distributed learning across enterprises and enable platform developers to create a secure, privacy-preserving offering for multiparty collaboration utilizing homomorphic encryption or differential privacy. The SDK is a lightweight, flexible, and scalable Python package, and allows researchers to bring their data science workflows implemented in any training libraries (PyTorch, TensorFlow, XGBoost, or even NumPy) and apply them in real-world FL settings. This paper introduces the key design principles of FLARE and illustrates some use cases (e.g., COVID analysis) with customizable FL workflows that implement different privacy-preserving algorithms.(Code is available at https://anonymous.4open.science/r/anon-flare.)

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