联邦学习应用技术研究综述

doi:10.3969/j.issn.1671-1122.2024.12.003

摘要/Abstract

摘要：

人工智能在训练推理等过程中的隐私泄露、推理失真等安全问题，引起了人们的高度关注，甚至涉及意识形态乃至国家战略安全。在此背景下，联邦学习作为一种新兴的机器学习架构，通过保持数据本地性的同时实现模型的联合训练，为多方参与数据分析、处理和共享的应用领域提供了有效的隐私保护能力。从联邦学习的研究动机、技术方法等方面来看，如何利用该技术有效解决典型应用场景下的实际问题是其核心和关键，因此相关应用研究现状的全面综述，对联邦学习的进一步研究与实践都具有参考价值。为此，文章对联邦学习在异常检测、推荐系统以及自然语言处理等典型技术应用中的研究现状进行综合性调研。首先，文章对相关文献按照应用场景角度进行全面的分类梳理，从多领域视角分析了联邦学习架构的研究现状。其次，文章从技术实现的角度，对比分析了各技术领域中不同方案的数据集合、性能特点、评价指标等方面。在此基础上，文章分析总结了联邦学习研究尤其是系统应用面临的关键挑战和发展方向。

关键词: 联邦学习, 异常检测, 推荐系统, 自然语言处理

Abstract:

Security problems, such as privacy leakage and reasoning distortion, arising from training and reasoning in AI have heightened concerns, even involving ideology and national strategic security. As an emerging machine learning architecture, federated learning provides effective privacy protection capabilities for multi-party data analysis, processing, and sharing by achieving global model training while maintaining private data locality. Then, from the perspective of research motivation, technical methods, and other aspects of federated learning, how to apply this technology in typical application scenarios to solve practical problems effectively is its core. Therefore, this article conducted a comprehensive survey on the current research status of application technology of federated learning in typical scenarios, which would be valuable to further research and practice of federated learning. Firstly, a comprehensive classification and sorting of relevant literature were conducted from the perspective of research application scenarios, and the research status in each scenario was analyzed from a multidisciplinary perspective. Secondly, from the perspective of technical implementation, a comparative analysis was conducted on the data sets, performance characteristics, evaluation indicators, and other aspects of different schemes in various application scenarios. Finally, the key challenges and development directions faced by federated learning research, especially system applications, were analyzed and summarized.

Key words: federated learning, abnormal detection, recommendation system, natural language processing

中图分类号:

TP309

何泽平, 许建, 戴华, 杨庚. 联邦学习应用技术研究综述[J]. 信息网络安全, 2024, 24(12): 1831-1844.

HE Zeping, XU Jian, DAI Hua, YANG Geng. A Review of Federated Learning Application Technologies[J]. Netinfo Security, 2024, 24(12): 1831-1844.

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工作	应用场景	数据集	优点	性能指标
AMCNN-LSTM^[15]	工业物联网中的时间序列数据	Four real-world data sets, power demand, space shuttle, ECG, engine	通信效率高	准确率
FedAnomaly^[16]	网络物理系统中的高维多元时间序列数据	SMAP, MSL, SWaT	连续异常段检测和检测延迟低	准确率
FL-IIOT^[17]	工业控制系统	SCADA	计算速度快	准确率、精确度
FL-LSTM^[18]	网络系统	SEA	提供更丰富的语义信息	准确率、精确度
FL-KMC^[19]	工业传感器	pump sensor dataset taken from 53 sensors	收敛速度快	准确率、精确度
FedAGRU^[20]	无线边缘网络	KDDCUP 99, CICIDS2017	强鲁棒性，降低通信成本	准确率、精确度
FL-AD^[21]	物联网	Modbus	攻击检测的准确率更高且误报率更小	准确率、精确度

工作	应用场景	数据集	优点	性能指标
FL-MGVN^[22]	工业控制系统中的异常检测	MNIST, Fashion-MNIST	更高的识别性能和分类精度	AUC分数
FATRAF^[23]	工业控制系统中的时间序列数据	Gas Pipeline, SWaT, HAI, Power Demand	动态跟踪新异常模式	准确率、精确度
FL-AD^[24]	本地数据交换受到限制且存在异构性的移动环境	MNIST, Fashion-MNIST	适用于本地数据交换受限且存在异构性的移动环境	AUC分数
DAEF^[25]	边缘计算	Covertype, Credit card, Shuttle	计算速度快，通信成本低	准确率、精确度
Fed-ANIDS^[26]	分布式网络入侵检测系统	USTC-TFC2016, CIC-IDS2017	优于其他基于生成对抗网络的模型	准确率、精确度

工作	应用场景	数据集	优点	性能指标
MT-DNN-FL^[27]	网络异常检测	CICIDS2017, ISCXVPN2016, ISCXTor2016	减少训练时间开销	精确度、召回率和准确率
FL-FFDF^[28]	人脸伪造视频检测	FaceForensics++, Celeb-DeepFake v2	高安全性和隐私保障、强鲁棒性	准确率、AUC
FLAD^[29]	工业物联网异常检测	smart terminals	高吞吐量、低延迟和高异常检测精度	准确率、精确度
HFL^[30]	网络异常检测	UNSW-NB15	降低通信成本，泛化性强	准确率
FL-finder^[31]	未知网络异常检测	KDD CUP 99, UNSW-NB 15	实时上报新的异常，能够缓存检测到的未知异常	准确率和F1分数
FL-SMAD^[32]	智能电网	KDD, NSL-KDD, CIDDS	降低通信成本	精确度、召回率和准确率
FDRL-IDS^[33]	入侵检测系统	ISOT-CID, NSL-KDD	可扩展性和鲁棒性	准确率、精确度、假阳性率

工作	数据集	优点
FL-GNN^[41]	MovieLens11, Flixster, Douban, YahooMusic	应用差分隐私技术
PerFedRec^[42]	MovieLens-100K, Yelp, Amazon-Kindle	降低通信成本
FeSoG^[43]	Ciao, Epinions, Filmtrust	采用局部GNN的关系处理、本地用户嵌入推理和伪标签技术来应对异构性、个性化和隐私保护挑战
FedRule^[44]	smart home devices from the Wyze Labs’1 rule engine	采用方差减少机制解决了Non-IID数据分布问题
FL-GMT^[45]	Ciao, Epinions	采用损失注意力的动态更新方法来提高聚合结束时的系统稳定性
FGC^[46]	MovieLens 100K, MovieLens 1M	通过模型聚合来抵抗投毒攻击
GCAFM^[47]	Douban, FilmTrust	通过因子分解机模型进行特征交叉

工作	数据集	优点
MVMF^[48]	MovieLens-1M, BookCrossings	解决冷启动问题和提升推荐质量
PrivMVMF^[49]	MovieLens-1M	引入同态加密和随机响应技术保护隐私
FL-UserRes^[50]	Filmtrust, Movielens-100k	引入同态加密和随机响应技术保护隐私
FMFParking^[51]	the real ETC parking lot data in Zhengzhou City, Henan Province, China	引入加密技术保护隐私
MetaMF^[52]	Douban, Hetrec-movielens, Movielens1M, Ciao	计算简单
FPL^[53]	Foursquare	用户可以控制泄露的敏感数据的数量
FL-FairRes^[54]	MovieLens-1M1, Amazon-Movies2	引入差分隐私技术保护隐私，降低通信开销
LightFR^[55]	MovieLens-1M, Filmtrust, Douban-Movie, Ciao	速度快、鲁棒性强