ZTE Communications ›› 2020, Vol. 18 ›› Issue (2): 11-19.doi: 10.12142/ZTECOM.202002003
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SHI Wenqi, SUN Yuxuan, HUANG Xiufeng, ZHOU Sheng(), NIU Zhisheng
Received:
2020-02-10
Online:
2020-06-25
Published:
2020-08-07
About author:
SHI Wenqi received his B.S. degree in electronic engineering from Tsinghua University, China in 2017. He is pursuing his Ph.D. degree in electronic engineering with Tsinghua University. His research interests include edge computing, machine learning and machine learning applications in wireless communications.|SUN Yuxuan received her B.S. degree in telecommunications engineering from Tianjin University, China, in 2015. She is currently working toward the Ph.D. degree in electronic engineering with Tsinghua University. Her research interests include mobile edge computing, vehicular cloud computing and distributed machine learning.|HUANG Xiufeng received his B.S. degree in electronic engineering from Tsinghua University, China, in 2018. He is currently a Ph.D. student in electronic engineering with Tsinghua University. His research interests include machine learning, edge computing and performance optimization for machine learning applications in wireless networks.|ZHOU Sheng (Supported by:
SHI Wenqi, SUN Yuxuan, HUANG Xiufeng, ZHOU Sheng, NIU Zhisheng. Scheduling Policies for Federated Learning in Wireless Networks: An Overview[J]. ZTE Communications, 2020, 18(2): 11-19.
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Table 1
Summary of recent papers on analog aggregation"
Technology | Highlights | Related Works |
---|---|---|
Power alignment | · Fundamental tradeoffs under Rayleigh fading channel | Ref. [ |
· Online energy-aware dynamic device scheduling policy | Ref. [ | |
· Device scheduling for multi-antenna analog aggregation | Ref. [ | |
Sparsification and error accumulation | · Gradient sparsification and error accumulation · Device scheduling policy under average power constraint | Refs. [26–27] |
Data redundancy | · Introducing data redundancy to deal with non-independent and identically distributed (non-i.i.d.) data | Ref. [ |
Table 2
Summary of recent papers on digital aggregation"
Technology | Highlights | Related Works |
---|---|---|
Aggregation frequency adaption | · Global aggregation frequency adaption under given resource constraints. | Ref. [ |
· Extending Ref. [ | Ref. [ | |
Local accuracy tuning | · Tuning local model accuracy to balance the tradeoff between local update and global aggregation · Energy- and convergence-aware resource allocation | Refs. [31–32] |
Device scheduling | · Energy- and convergence-aware joint scheduling and resource allocation | Ref. [ |
·Consider unreliable wireless transmissions | Refs. [35–36] | |
· Maximize the convergence rate with respect to time | Refs. [ |
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