On-device Federated Learning with Flower
arXiv:2104.03042
Abstract
Federated Learning (FL) allows edge devices to collaboratively learn a shared prediction model while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store data in the cloud. Despite the algorithmic advancements in FL, the support for on-device training of FL algorithms on edge devices remains poor. In this paper, we present an exploration of on-device FL on various smartphones and embedded devices using the Flower framework. We also evaluate the system costs of on-device FL and discuss how this quantification could be used to design more efficient FL algorithms.
arXiv commentAccepted at the 2nd On-device Intelligence Workshop @ MLSys 2021. arXiv admin note: substantial text overlap with arXiv:2007.14390
References in corpus (10)
- Communication-Efficient Learning of Deep Networks from Decentralized Data
- Federated Optimization in Heterogeneous Networks
- FedML: A Research Library and Benchmark for Federated Machine Learning
- A generic framework for privacy preserving deep learning
- Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes
- LEAF: A Benchmark for Federated Settings
- TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems
- Flower: A Friendly Federated Learning Research Framework
- Visual Wake Words Dataset
- Heterogeneous Bitwidth Binarization in Convolutional Neural Networks