QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning
Liping Yi, Wang Gang, Liu Xiaoguang
Abstract
In cross-device Federated Learning (FL), the communication cost of transmitting full-precision models between edge devices and a central server is a significant bottleneck, due to expensive, unreliable, and low-bandwidth wireless connections. As a solution, we propose a novel FL framework named QSFL, towards optimizing FL uplink (client-to-server) communication at both client and model levels. At the client level, we design a Qualification Judgment (QJ) algorithm to sample high-qualification clients to upload models. At the model level, we explore a Sparse Cyclic Sliding Segment (SCSS) algorithm to further compress transmitted models. We prove that QSFL can converge over wall-to-wall time, and develop an optimal hyperparameter searching algorithm based on theoretical analysis to enable QSFL to make the best trade-off between model accuracy and communication cost. Experimental results show that QSFL achieves state-of-the-art compression ratios with marginal model accuracy degradation.
BibTeX
@InProceedings{pmlr-v162-yi22a,
title = {{QSFL}: A Two-Level Uplink Communication Optimization Framework for Federated Learning},
author = {Yi, Liping and Gang, Wang and Xiaoguang, Liu},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {25501--25513},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/yi22a/yi22a.pdf},
url = {https://proceedings.mlr.press/v162/yi22a.html},
abstract = {In cross-device Federated Learning (FL), the communication cost of transmitting full-precision models between edge devices and a central server is a significant bottleneck, due to expensive, unreliable, and low-bandwidth wireless connections. As a solution, we propose a novel FL framework named QSFL, towards optimizing FL uplink (client-to-server) communication at both client and model levels. At the client level, we design a Qualification Judgment (QJ) algorithm to sample high-qualification clients to upload models. At the model level, we explore a Sparse Cyclic Sliding Segment (SCSS) algorithm to further compress transmitted models. We prove that QSFL can converge over wall-to-wall time, and develop an optimal hyperparameter searching algorithm based on theoretical analysis to enable QSFL to make the best trade-off between model accuracy and communication cost. Experimental results show that QSFL achieves state-of-the-art compression ratios with marginal model accuracy degradation.}
}