A Survey of Federated Learning on Non-IID Data

doi:10.12142/ZTECOM.202203003

Abstract

Abstract:

Federated learning (FL) is a machine learning paradigm for data silos and privacy protection，which aims to organize multiple clients for training global machine learning models without exposing data to all parties. However, when dealing with non-independently identically distributed (non-IID) client data, FL cannot obtain more satisfactory results than centrally trained machine learning and even fails to match the accuracy of the local model obtained by client training alone. To analyze and address the above issues, we survey the state-of-the-art methods in the literature related to FL on non-IID data. On this basis, a motivation-based taxonomy, which classifies these methods into two categories, including heterogeneity reducing strategies and adaptability enhancing strategies, is proposed. Moreover, the core ideas and main challenges of these methods are analyzed. Finally, we envision several promising research directions that have not been thoroughly studied, in hope of promoting research in related fields to a certain extent.

Key words: data heterogeneity, federated learning, non-IID data

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.

Figures/Tables 8

References 41

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Methods	Ways	Advantages	Disadvantages
Data preprocessing	Direct	? Easy to implement	? May reveal privacy ? Proxy dataset required
Data preprocessing	Indirect	? Strong privacy	? Contextual information may be required ? More complex to implement
Client selection	Context-based	? Faster model converges	? May reveal privacy
Client selection	Deep-learning-based	? No context required ? Better effect	? Higher time and space costs

Methods	Ways	Advantages	Disadvantages
Data preprocessing	Direct	? Easy to implement	? May reveal privacy ? Proxy dataset required
Data preprocessing	Indirect	? Strong privacy	? Contextual information may be required ? More complex to implement
Client selection	Context-based	? Faster model converges	? May reveal privacy
Client selection	Deep-learning-based	? No context required ? Better effect	? Higher time and space costs

Methods	Ways	Advantages	Disadvantages
Federated multitask learning	Client-based	? Easy to implement	? Possibility to isolate heterogeneous clients
Federated multitask learning	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
Federated clustering learning	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
Federated knowledge distillation	Mutual distillation	? Robust to heterogeneous models ? Suitable for a large number of clients	? Negative transfer possible ? Lack of theoretical analysis

Methods	Ways	Advantages	Disadvantages
Federated multitask learning	Client-based	? Easy to implement	? Possibility to isolate heterogeneous clients
Federated multitask learning	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
Federated clustering learning	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
Federated knowledge distillation	Mutual distillation	? Robust to heterogeneous models ? Suitable for a large number of clients	? Negative transfer possible ? Lack of theoretical analysis

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