Prophet: Proactive Candidate-Selection for Federated Learning by Predicting the Qualities of Training and Reporting Phases

by   Huawei Huang, et al.

Federated Learning (FL) is viewed as a promising technique for future distributed machine learning. It permits a large number of mobile devices participating in the training of a global model collaboratively without having to expose their local private data. Although the challenge of the network connection will be much relieved in 5G/B5G era, the training latency is still an obstacle preventing FL from being largely adopted. One of the most fundamental problems that leads to large training latency is the bad candidate-selection of FL participants. To the best of our knowledge, the existing candidate-selection algorithms belong to the reactive manner. Under such reactive selection, the FL parameter server only knows the currently-observed resources of all candidates. In the dynamic FL environment, the mobile devices selected by the reactive candidate-selection algorithms very possibly fail to complete the training and reporting phases of FL. To this end, we study the proactive candidate-selection for FL in this paper. We first let each candidate device locally predict the qualities of both its training and reporting phases using the LSTM network. Then, the proposed candidate-selection algorithm is implemented by the Deep Reinforcement Learning (DRL) framework, which can adapt to the dynamically varying factors in the metropolitan edge computing environment. Finally, the real-world trace-driven experiments prove that the proposed proactive approach outperforms the existing reactive algorithms with respect to the ratio of valid participants and the test accuracy of the aggregated global FL model.


page 1

page 9


Multi-Tier Client Selection for Mobile Federated Learning Networks

Federated learning (FL), which addresses data privacy issues by training...

A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning

Federated learning (FL) is a training technique that enables client devi...

Flower: A Friendly Federated Learning Research Framework

Federated Learning (FL) has emerged as a promising technique for edge de...

On-the-fly Resource-Aware Model Aggregation for Federated Learning in Heterogeneous Edge

Edge computing has revolutionized the world of mobile and wireless netwo...

Online Data Selection for Federated Learning with Limited Storage

Machine learning models have been deployed in mobile networks to deal wi...

Dap-FL: Federated Learning flourishes by adaptive tuning and secure aggregation

Federated learning (FL), an attractive and promising distributed machine...

FLIPS: Federated Learning using Intelligent Participant Selection

This paper presents the design and implementation of FLIPS, a middleware...

Please sign up or login with your details

Forgot password? Click here to reset