Optimization-Based GenQSGD for Federated Edge Learning

被引:0
|
作者
Li, Yangchen [1 ]
Cui, Ying [1 ]
Lau, Vincent [2 ]
机构
[1] Shanghai Jiao Tong Univ, Shanghai, Peoples R China
[2] HKUST, Hong Kong, Peoples R China
来源
2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) | 2021年
基金
上海市自然科学基金;
关键词
Federated learning; stochastic gradient descent; optimization; algorithm design; convergence analysis;
D O I
10.1109/GLOBECOM46510.2021.9685591
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Optimal algorithm design for federated learning (FL) remains an open problem. This paper explores the full potential of FL in practical edge computing systems where workers may have different computation and communication capabilities, and quantized intermediate model updates are sent between the server and workers. First, we present a general quantized parallel mini-batch stochastic gradient descent (SGD) algorithm for FL, namely GenQSGD, which is parameterized by the number of global iterations, the numbers of local iterations at all workers, and the mini-batch size. We also analyze its convergence error for any choice of the algorithm parameters. Then, we optimize the algorithm parameters to minimize the energy cost under the time constraint and convergence error constraint. The optimization problem is a challenging non-convex problem with non-differentiable constraint functions. We propose an iterative algorithm to obtain a KKT point using advanced optimization techniques. Numerical results demonstrate the significant gains of GenQSGD over existing FL algorithms and reveal the importance of optimally designing FL algorithms.
引用
收藏
页数:6
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