联邦推荐系统综述

doi:10.11959/j.issn.2096-0271.2022032

Abstract

Abstract:

In the federated learning (FL) paradigm, the original data are stored in independent clients while masked data are sent to a central server to be aggregated, which proposes a novel design approach to numerous domains.Given the wide application of recommendation systems (RS) in diverse domains, combining RS with FL techniques has been gaining momentum to reduce the computational cost, do cross-domain recommendation and protect users’ privacy while maintaining recommendations performance as traditional RS.The federated learning-based recommendation systems in recent years were comprehensively summarized.The difference between traditional and federated recommendation systems was analyzed, and the main research direction and progress of federated recommendation systems were demonstrated with comparison and analysis.Firstly, the traditional recommendation systems and their bottleneck were summarized.Then the federated learning paradigm was introduced.Furthermore, the advantages of combining federated learning with recommendation systems were depicted in two aspects: privacy protection and usage of multi-domain user information, along with the technical challenges during the combination.At the same time, the existing deployment of federated recommendation systems was illustrated in detail.Finally, future research on federated recommendation systems was prospected and summarized.

Key words: federated learning, recommendation system, privacy-preserving, collaborative filtering, deep learning

CLC Number:

TP391

Zhitao ZHU, Shijing SI, Jianzong WANG, Jing XIAO. Survey on federated recommendation systems[J]. Big Data Research, 2022, 8(4): 105-132.

Figures/Tables 10

相似度度量	表达式	优点	缺点
闵可夫斯基距离	$dist (X, Y) = {(\sum_{i = 1}^{n} \| x_{i} - y_{i} \|^{p})}^{\frac{1}{p}}$	反映个别数值特征的绝对差异	受离群值影响较大
杰卡德相似系数	$J (A, B) = \frac{\| A \cap B \|}{\| A \cup B \|}$	计算简单，算力负担小	没有考虑用户之间的评分差异；精度不高
余弦相似度	$\cos (θ) = \frac{a^{⊤} b}{\| a \| \cdot \| b \|}$	对绝对值不敏感，修正了用户之间可能存在的参数差异	将缺失值设置为0会缩小真实差距
皮尔逊相关系数	$ρ_{X Y} = \frac{\sum_{i = 1}^{n} (X - μ_{X}) (Y - μ_{Y})}{\sqrt{\sum_{i = 1}^{n} (X - μ_{X})^{2}} \sqrt{\sum_{i = 1}^{n} (Y - μ_{Y})^{2}}}$	用平均值填充缺失的值，以避免分数膨胀	不能直接反映所使用的物品数量；在稀疏的数据集上不易计算相似度

分类	表示
横向联邦学习	$X_{i} = X_{j}, Y_{i} = Y_{j}, I_{i} \neq I_{j} \forall D_{i}, D_{j}, i \neq j$
纵向联邦学习	$X_{i} \neq X_{j}, Y_{i} \neq Y_{j}, I_{i} = I_{j} \forall D_{i}, D_{j}, i \neq j$
联邦迁移学习	$X_{i} \neq X_{j}, Y_{i} \neq Y_{j}, I_{i} \neq I_{j} \forall D_{i}, D_{j}, i \neq j$

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贡献与挑战	方面	方法	参考文献
联邦推荐系统的贡献	隐私保护	加密方法	[53,71]
		数据扰动	[63,65,68-72]
	跨领域推荐	跨领域用户信息	[73,75]
		跨模态用户数据	[76-77]
落地过程中的挑战	数据异质性	设备异质性	[83-85]
		统计异质性	[1,86-87]
		模型异质性	[88-89,95-100]
	通信成本	通信优化方法	[90,94,101-104]

模型	介绍	解决问题	局限性
FCF^[106]	针对隐反馈数据首先提出联邦协同过滤算法	在保护隐私数据的前提下共同训练协同过滤推荐模型	需要所有客户端参与；存在冷启动问题；存在梯度泄露风险
SFMF^[107]	加入同态加密进行安全矩阵分解	防止梯度泄露	在隐私保护力度与算力开销之间难以两全
Stronger FCF^[108]	使用本地差分隐私与代理服务器保护物品更新梯度矩阵	防止第三方获取物品更新梯度矩阵实施重构攻击	仅在隐反馈推荐情景下证明了有效性
FedRec ^[109]、FedRec++^[110]	适用于显反馈评分数据的联邦协同过滤，后者增加了消除噪声的机制	对基于显反馈数据的推荐系统进行了联邦化实践	与现代推荐算法的结合不多
FedNewsRec^[111]	结合深度网络学习新闻表征与用户历史，产生新闻推荐	解决冷启动问题；不需要所有用户参与	非通用模型
FL-MV-DSSM^[113]	多视图深度结构化语义模型	解决冷启动问题；多视图	带来新的安全挑战
MetaMF^[115]	引入元学习矩阵分解训练个性化模型	快速适应新用户	仅适用于评分预测任务
FedFast^[116]	在客户端选择与更新信息聚合方面提出改善方法	加快模型收敛，减少计算量	带来额外的通信开销

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