1 research outputs found
Prophet: Proactive Candidate-Selection for Federated Learning by Predicting the Qualities of Training and Reporting Phases
Although the challenge of the device connection is much relieved in 5G
networks, the training latency is still an obstacle preventing Federated
Learning (FL) from being largely adopted. One of the most fundamental problems
that lead to large latency is the bad candidate-selection for FL. In the
dynamic environment, the mobile devices selected by the existing reactive
candidate-selection algorithms very possibly fail to complete the training and
reporting phases of FL, because the FL parameter server only knows the
currently-observed resources of all candidates. To this end, we study the
proactive candidate-selection for FL in this paper. We first let each candidate
device predict the qualities of both its training and reporting phases locally
using LSTM. Then, the proposed candidateselection algorithm is implemented by
the Deep Reinforcement Learning (DRL) framework. Finally, the real-world
trace-driven experiments prove that the proposed approach outperforms the
existing reactive algorithmsComment: We found significant technique errors in our previous version. The
proposed DRL-based algorithm cannot solve the large-scale scheduling for
federated learning. For the health of relevant research communities, we
decide to withdraw our submissio