Oort: Efficient Federated Learning via Guided Participant Selection
This addresses efficiency and accuracy issues in federated learning for developers and users at scale, representing a novel method rather than an incremental improvement.
The paper tackles the problem of poor model and system efficiency in federated learning due to random participant selection by proposing Oort, a guided selection method that prioritizes clients with high data utility and fast training capabilities. The result shows improvements in time-to-accuracy performance by 1.2x-14.1x and final model accuracy by 1.3%-9.8% compared to existing mechanisms.
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.