Neursafe-FL: A Reliable, Efficient, Easy-to- Use Federated Learning Framework

doi:10.12142/ZTECOM.202203006

ZTE Communications ›› 2022, Vol. 20 ›› Issue (3): 43-53.DOI: 10.12142/ZTECOM.202203006

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Neursafe-FL: A Reliable, Efficient, Easy-to- Use Federated Learning Framework

TANG Bo¹, ZHANG Chengming¹, WANG Kewen¹, GAO Zhengguang², HAN Bingtao²()

^1.ZTE Corporation, Shenzhen 518057, China
^2.The State Key Laboratory of Mobile Network and Mobile Multimedia Technology, Shenzhen 518055, China

Received:2022-06-18 Online:2022-09-13 Published:2022-09-14
About author:TANG Bo received his master's degree from Northwestern Polytechnical University, China in 2005. Currently, as the system architect of ZTE Corporation, he is mainly responsible for federated learning and AI security solutions. His current research interests include container and container cloud, heterogeneous resource scheduling, and AI security and trustworthy AI. He holds several patents in the above research areas.|ZHANG Chengming received his BS degree from Yangzhou University, China in 2011, and ME degree from Nanjing University of Posts and Telecommunications, China in 2015. He is a senior R&D engineer of ZTE Corporation. His current research interests include AI platform, container cloud, resource scheduling, federated learning, and AI security.|WANG Kewen received his BS degree from China University of Mining and Technology, China in 2015, and the ME degree from Beijing Institute of Technology, China in 2017. He is an AI senior algorithm engineer of ZTE Corporation. His current research interests include AI Systems and AI security, such as federated learning, adversarial attack, and defense in deep learning.|GAO Zhengguang received his PhD degree from Beijing University of Posts and Telecommunications, China in 2020. He was a visiting PhD student in High Performance Networks Group, University of Bristol, UK from 2018 to 2019. After graduation, he was selected for “LAN JIAN” program of ZTE Corporation as an algorithm researcher. His current research interests include 5G/6G communication technologies, mobile networks, and machine learning for future communications.|HAN Bingtao (han.bingtao@zte.com.cn) received his BS degree from Tianjin University, China in 2001, and MS degree from Nankai University, China in 2004. He is the deputy director of the State Key Laboratory of Mobile Network and Mobile Multimedia Technology, and the leader for “Adlik” project of the LF AI & Data Foundation. Currently, he is the AI system architect of Central R&D Institute, ZTE Corporation. His current research interests include deep learning algorithms, AI systems, and network intelligence. He is the author and co-author for numbers of patents and related monographs.

Abstract

Abstract:

Federated learning (FL) has developed rapidly in recent years as a privacy-preserving machine learning method, and it has been gradually applied to key areas involving privacy and security such as finance, medical care, and government affairs. However, the current solutions to FL rarely consider the problem of migration from centralized learning to federated learning, resulting in a high practical threshold for federated learning and low usability. Therefore, we introduce a reliable, efficient, and easy-to-use federated learning framework named Neursafe-FL. Based on the unified application program interface (API), the framework is not only compatible with mainstream machine learning frameworks, such as Tensorflow and Pytorch, but also supports further extensions, which can preserve the programming style of the original framework to lower the threshold of FL. At the same time, the design of componentization, modularization, and standardized interface makes the framework highly extensible, which meets the needs of customized requirements and FL evolution in the future. Neursafe-FL is already on Github as an open-source project¹.

Key words: federated learning, privacy-preserving, Neursafe-FL

TANG Bo, ZHANG Chengming, WANG Kewen, GAO Zhengguang, HAN Bingtao. Neursafe-FL: A Reliable, Efficient, Easy-to- Use Federated Learning Framework[J]. ZTE Communications, 2022, 20(3): 43-53.

Figures/Tables 15

Figure 1 Federated learning scenarios

Table 1 Comparison of open source frameworks

Concerns	Features	TFF	PySyft	FedML	FATE	PaddleFL	Neursafe-FL
Supported running mode	Standalone	√	√	√	√	√	√
	Cross-device	×	×	√	×	×	√
	Cross-silo	×	×	√	√	√	√
Aggregation algorithms	IID (FedAvg, etc.)	√	√	√	√	√	√
Aggregation algorithms	Non-IID (FedProx, etc.)	×	×	√	√	-	√
Supported underlying framework	Tensorflow	√	√	×	√	×	√
Supported underlying framework	Pytorch	×	√	√	√	×	√
Privacy protection methods	DP	√	√	√	×	√	√
	MPC	×	√	×	√	√	√
	HE	×	√	×	√	×	×
Flexibility	Device management	×	×	×	×	×	√
Flexibility	Customization	×	×	√	×	×	√

Figure 2 Example of model migration for MNIST

Figure 3 Multi-running mode

Figure 4 Architecture of Neursafe-FL

Figure 5 Interaction between Neursafe-FL core components

Figure 6 Comparison of federated training and centralized training

Figure 7 Convergence comparison of federated training with different client numbers

Figure 8 Results of different aggregation algorithms under non-IID data

Figure 9 A comparative test of introducing DP and not introducing differential privacy

Table 2 Results of different parameters for differential privacy

Type	Round Number	Noise_multiplier	Time Cost/s	Accuracy
Without DP	100	-	2 873.22	0.914 3
Without DP	50	-	1 396.56	0.898 4
With DP	100	0.01	2 879.36	0.912 2
	100	0.005	2 882.86	0.891 0
	50	0.01	1 390.42	0.876 3

Figure 10 A comparative test of federated training with SSA and without SSA

Figure 11 Impacts of SSA in the scenario where the clients are disconnected

Figure 12 Impacts of client’s number on the performance of SSA

Figure 13 Impacts of different dropout rates on the performance of federated training

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Neursafe-FL: A Reliable, Efficient, Easy-to- Use Federated Learning Framework

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