Journal on Communications ›› 2021, Vol. 42 ›› Issue (6): 195-212.doi: 10.11959/j.issn.1000-436x.2021120
Special Issue: 联邦学习
• Comprehensive Review • Previous Articles Next Articles
Youhuizi LI1, Yuyu YIN1, Honghao GAO2,3, Yi JIN4, Xinheng WANG5
Revised:
2021-03-10
Online:
2021-06-25
Published:
2021-06-01
Supported by:
CLC Number:
Youhuizi LI, Yuyu YIN, Honghao GAO, Yi JIN, Xinheng WANG. Survey on privacy protection in non-aggregated data sharing[J]. Journal on Communications, 2021, 42(6): 195-212.
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类型 | 文献及协议 | 应用场景 | 安全 | 支持多方 | 输出 | 运行时间/s (输入元素个数为216) | 输入上限k (输入元素个数为2k) | 备注 | |
文献[ | O-PSI | 云外包 | 半诚实 | √ | 交集 | — | 20 | — | |
同态加密 | EO-PSI | 415.8 | |||||||
文献[ | PSI | 云外包 | 半诚实 | × | 交集大小 | 1 670 | 20 | — | |
文献[ | PSI | 本地 | 半诚实 | √ | 交集 | 32.78 | 19 | — | |
文献[ | Basic | 本地 | 半诚实 | × | 交集 | 13.767 | 20 | LAN | |
逻辑电路 | Advanced | 9.763 | |||||||
文献[ | Separate | 本地 | 半诚实 | × | 交集 | 11.251 | 20 | LAN | |
Combined | 9.076 | 1 Thread | |||||||
文献[ | PSI | 本地 | 半诚实 | × | 交集 | 0.63 | 24 | LAN | |
恶意 | |||||||||
文献[ | Spot-low | 本地 | 半诚实 | × | 交集 | 13.7 | 24 | 100 Mbit/s | |
不经意传输 | Spot-fast | 恶意 | 2.91 | 1 Thread | |||||
文献[ | PSI | 本地 | 半诚实 | × | 交集 | 0.702 | 24 | LAN | |
1 Thread | |||||||||
文献[ | PSI | 本地 | 恶意 | × | 交集 | 9.7 | 20 | LAN | |
文献[ | EC-ROM | 本地 | 恶意 | × | 交集 | 0.94 | 24 | LAN | |
DE-ROM | 1.3 | 1 Thread | |||||||
注:O-PSI在输入为215时,运行时间达到57 357 s,时间过大,故未标出。 |
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解决方法 | 文献 | 核心方法/特点 | 局限性 |
文献[ | 根据值函数选择一个优化(有用程度较高)的数据子集 | 客户不断收集(并可能获取)数据,并且在许多情况下,分发可能是不稳定的 | |
参与者选择 | 文献[ | 主动管理客户端,FedCS为客户端设置了在FL(federated learning)协议中下载、更新和上载ML模型的最后期限 | 在动态的场景中工作平均资源量以及更新和上载所需的时间会动态波动 |
文献[ | 只有极少数客户端(例如,少于1%)允许其数据上传到FL服务器。通过考虑每个客户端的数据分布和通道条件来计划数据上载客户端和模型上载客户端 | 考虑客户的能源消耗 | |
文献[ | 采用deepQ学习算法,该算法允许服务器学习和找到最佳决策 | 未考虑Non-IID数据 | |
文献[ | 契约理论 | 确保本地模型更新的可靠性 | |
奖励机制 | 文献[ | 将声誉与合同理论相结合的有效激励机制 | 只考虑了一个联邦服务器 |
文献[ | 斯塔克伯格博弈的平衡解 | 服务器获利较低,只考虑了一个联邦服务器 |
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名称 | 有损压缩 | 特点 | 下行压缩 | 对Non-IID数据有稳健性 | 压缩的倍数/通信时间缩小倍数(选取最好情况) | 测试数据集 |
深度梯度压缩[ | × | 动量修正、局部梯度剪裁、动量因子映射 | × | √ | 压缩了270~600倍 | CIFAR10、ImageNet、Penn Treebank、AN4、Librispeech |
量化SGD[ | √ | 量化、黑盒压缩 | × | × | 训练减少了2.5~6.8倍 | ImageNet、CIFAR10、MNIST、CMU AN4、AlexNet、VGG、ResNet |
PowerSGD[ | √ | 功率迭代、快速 | × | × | 压缩了24~128倍 | CIFAR10 |
SignSGD[ | √ | top-k梯度稀疏化,实现下游压缩以及权重更新的分层和最佳Golomb编码 | √ | √ | 压缩了1 050倍 | CIFARA、KWSA、F-MNIST |
稀疏三进制压缩[ | √ | 多数投票的方式 | √ | × | 速度提高25% | ImageNet、RESNET50 |
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算法—框架名称 | Baseline | 特点 | 通信情况 | 隐私保护 | 适用场景 |
FedAvg[ | FeedSGD | 迭代平均 | 与同步随机梯度下降相比,通信效率提高10~100倍 | DP、MPC | 非平衡数据non-IID数据 |
DSVGR[ | SVRG DANE | 稀疏数据(结构) | — | — | 不平衡数据可用节点少 |
Parallel Restarted SGD[ | Parallel mini-batch SGD parallel SGD | 并行、重启 | 与parallel mini-batch SGD相比减少了O(T1/4)的通信交流次数 | — | 非凸优化 |
PASGD[ | Fully synchronous SGD | 周期平均 | VGG-16中,ADACOMIS比全同步SGD快3.5倍;ResNet-50中,ADACOMM比完全同步SGD快2倍 | — | — |
AFL[ | Active Learning | 主动、选择性抽样迭代 | 减少20%~70%的迭代 | 有 | — |
AFL [ | FedAvg | 分布、公平 | — | — | 集中式模型 |
Fmore[ | — | 激励机制,迁移学习 | 减少近51.3%的训练轮数,真实系统,时间减少了38.4% | — | 移动边缘计算 |
FedBCD[ | — | 基于块坐标梯度下降 | 可以显著降低通信成本 | 有 | 共享样本 |
MMD[ | 单流 | 双流模型代替单一模型 | 非IID数据分布所需的通信轮数减少了20%以上 | — | non-IID数据 |
Mini-batch SGD[ | SGDZ | 小型批量 | 通信回合的数量最多可以减少T/2 (其中T表示总步数) | — | 高网络时延带宽受限 |
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框架 | 支持类型 | 安全协议 | 主要支持场景 | 软件 | Kubernetes | 开发机构 |
JUGO | 安全两方计算 | 同态加密 | 商业应用 | 不开源 | 不支持 | 矩阵元 |
混淆电路 | ||||||
Tasty | 安全多方计算 | PSI | 学术研究 | 开源 | 不支持 | — |
同态加密 | ||||||
混淆电路 | ||||||
PaddleFL | 横向联邦学习 | PSI | 学术研究 | 开源 | 仅Data Parallel支持 | 百度 |
纵向联邦学习 | 秘密分享 | |||||
同态加密 | ||||||
FATE | 横向联邦学习 | PSI | 商用数据共享 | 开源 | 支持 | 微众银行 |
纵向联邦学习 | 秘密分享 | |||||
联邦迁移学习 | 同态加密 | |||||
TTF | 横向联邦学习 | DP | 学术研究 | 开源 | 不支持 | 谷歌 |
GAN | ||||||
Clara | 横向联邦学习 | DP | 商业应用 | 不开源 | 支持 | 英伟达 |
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