Oort: Efficient Federated Learning via Guided Participant Selection

TL;DR

Oort is a federated learning participant selection method that enhances training efficiency and model accuracy by prioritizing clients based on data utility and device capability, scaling effectively to millions of devices.

Contribution

Oort introduces a guided participant selection approach that improves federated learning efficiency and accuracy, addressing the limitations of random client selection.

Findings

Improves time-to-accuracy by up to 14.1x

Enhances final model accuracy by up to 9.8%

Efficiently enforces testing data distribution requirements

Abstract

Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that enables in-situ model training and testing on edge data. Despite having the same end goals as traditional ML, FL executions differ significantly in scale, spanning thousands to millions of participating devices. As a result, data characteristics and device capabilities vary widely across clients. Yet, existing efforts randomly select FL participants, which leads to poor model and system efficiency. In this paper, we propose Oort to improve the performance of federated training and testing with guided participant selection. With an aim to improve time-to-accuracy performance in model training, Oort prioritizes the use of those clients who have both data that offers the greatest utility in improving model accuracy and the capability to run training quickly. To enable FL developers to interpret their results in model testing, Oort enforces their requirements on the distribution of participant data while improving the duration of federated testing by cherry-picking clients. Our evaluation shows that, compared to existing participant selection mechanisms, Oort improves time-to-accuracy performance by 1.2x-14.1x and final model accuracy by 1.3%-9.8%, while efficiently enforcing developer-specified model testing criteria at the scale of millions of clients.