ZTE Communications ›› 2022, Vol. 20 ›› Issue (3): 17-26.DOI: 10.12142/ZTECOM.202203003
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HAN Xuming1, GAO Minghan2, WANG Limin3(), HE Zaobo1, WANG Yanze1
Received:
2022-06-10
Online:
2022-09-13
Published:
2022-09-14
About author:
HAN Xuming received his PhD degree from Jilin University, China. Now he is a professor and PhD supervisor at Jinan University, China. He is in charge of about 10 important scientific research projects and 80 journal papers and conference papers, and has publish four academic monographs. His research interests include artificial intelligence, federated Learning, and machine learning.|GAO Minghan is currently a graduate student in Changchun University of Technology, China. His research interests include federated learning, multitasking optimization, and clustering.|WANG Limin (HAN Xuming, GAO Minghan, WANG Limin, HE Zaobo, WANG Yanze. A Survey of Federated Learning on Non-IID Data[J]. ZTE Communications, 2022, 20(3): 17-26.
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URL: http://zte.magtechjournal.com/EN/10.12142/ZTECOM.202203003
Figure 4 Heterogeneity reducing strategies and adaptability enhancing strategies: (a) Heterogeneity reducing strategies and (b) adaptability enhancing strategies
Methods | Ways | Advantages | Disadvantages |
---|---|---|---|
Data preprocessing | Direct | ? Easy to implement | ? May reveal privacy ? Proxy dataset required |
Indirect | ? Strong privacy | ? Contextual information may be required ? More complex to implement | |
Client selection | Context-based | ? Faster model converges | ? May reveal privacy |
Deep-learning-based | ? No context required ? Better effect | ? Higher time and space costs |
Table 1 Summary of specific methods based on heterogeneity reducing strategies
Methods | Ways | Advantages | Disadvantages |
---|---|---|---|
Data preprocessing | Direct | ? Easy to implement | ? May reveal privacy ? Proxy dataset required |
Indirect | ? Strong privacy | ? Contextual information may be required ? More complex to implement | |
Client selection | Context-based | ? Faster model converges | ? May reveal privacy |
Deep-learning-based | ? No context required ? Better effect | ? Higher time and space costs |
Figure 6 Method settings for adaptability enhancing strategies: (a) federated multitask learning, (b) federated clustering learning, and (c) federated knowledge distillation
Methods | Ways | Advantages | Disadvantages |
---|---|---|---|
Federated multitask learning | Client-based | ? Easy to implement | ? Possibility to isolate heterogeneous clients |
Subtask- division-based | ? Part-time joins allowed | ? Data quality sensitive | |
Federated clustering learning | Model-loss-based | ? Easy to implement ? Predictable effect | ? Need to preset the number of clusters ? Communication overhead is high |
Client-similarity- based | ? No need to preset the number of clusters | ? Lack of theoretical analysis | |
Federated knowledge distillation | One-way distillation | ? Strong privacy | ? Poor to heterogeneity model ? Contextual information may be required |
Mutual distillation | ? Robust to heterogeneous models ? Suitable for a large number of clients | ? Negative transfer possible ? Lack of theoretical analysis |
Table 2 Summary of specific methods based on adaptability enhancing strategies
Methods | Ways | Advantages | Disadvantages |
---|---|---|---|
Federated multitask learning | Client-based | ? Easy to implement | ? Possibility to isolate heterogeneous clients |
Subtask- division-based | ? Part-time joins allowed | ? Data quality sensitive | |
Federated clustering learning | Model-loss-based | ? Easy to implement ? Predictable effect | ? Need to preset the number of clusters ? Communication overhead is high |
Client-similarity- based | ? No need to preset the number of clusters | ? Lack of theoretical analysis | |
Federated knowledge distillation | One-way distillation | ? Strong privacy | ? Poor to heterogeneity model ? Contextual information may be required |
Mutual distillation | ? Robust to heterogeneous models ? Suitable for a large number of clients | ? Negative transfer possible ? Lack of theoretical analysis |
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