大数据 ›› 2022, Vol. 8 ›› Issue (4): 105-132.doi: 10.11959/j.issn.2096-0271.2022032
朱智韬1,2, 司世景1, 王健宗1, 肖京1
出版日期:
2022-07-15
发布日期:
2022-07-01
作者简介:
朱智韬(1996- ),男,中国科学技术大学硕士生,平安科技(深圳)有限公司算法工程师,中国计算机学会(CCF)会员,主要研究方向为人工智能、联邦学习和推荐系统等基金资助:
Zhitao ZHU1,2, Shijing SI1, Jianzong WANG1, Jing XIAO1
Online:
2022-07-15
Published:
2022-07-01
Supported by:
摘要:
在联邦学习范式中,原始数据被本地存储在独立的用户客户端中,而脱敏数据被发送到中心服务器中加以聚合,这给众多领域提供了一种新颖的设计思路。考虑到传统推荐系统的研究方向集中于提高推荐效果,在资源节约、跨领域推荐、隐私保护等方面还具有很大改进空间,如何将联邦学习与推荐系统结合以解决这些问题成为当前的一个研究热点。对近年来基于联邦学习的推荐系统进行了全面的总结、比较与分析,首先介绍了推荐系统的传统实现方式及面临的瓶颈;然后引入了联邦学习范式,描述了联邦学习在隐私保护、利用多领域用户数据两方面给推荐系统带来的增益,以及二者结合的技术挑战,进而详细说明了现有的联邦推荐系统部署方式;最后,对联邦推荐系统未来的研究进行了展望与总结。
中图分类号:
朱智韬, 司世景, 王健宗, 肖京. 联邦推荐系统综述[J]. 大数据, 2022, 8(4): 105-132.
Zhitao ZHU, Shijing SI, Jianzong WANG, Jing XIAO. Survey on federated recommendation systems[J]. Big Data Research, 2022, 8(4): 105-132.
表4
联邦推荐系统部署实践的优劣势"
模型 | 介绍 | 解决问题 | 局限性 |
FCF[ | 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[ | 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[ | 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 [ | 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[ | 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[ | 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[ | 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[ | 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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