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Author: beiyuouo

README.md

@@ -797,7 +797,7 @@ In this section, we will summarize Federated Learning papers accepted by top Dat
 | Efficient Participant Contribution Evaluation for Horizontal and Vertical Federated Learning | USTC | ICDE | 2022 | DIG-FL[^DIG-FL] | [[PUB](https://ieeexplore.ieee.org/document/9835159)] |
 | An Introduction to Federated Computation | University of Warwick; Facebook | SIGMOD Tutorial | 2022 | FCT[^FCT] | [[PUB](https://dl.acm.org/doi/10.1145/3514221.3522561)] |
 | BlindFL: Vertical Federated Machine Learning without Peeking into Your Data | PKU; Tencent | SIGMOD | 2022 | BlindFL[^BlindFL] | [[PUB](https://dl.acm.org/doi/10.1145/3514221.3526127)] [[PDF](https://arxiv.org/abs/2206.07975)] |
-| An Efficient Approach for Cross-Silo Federated Learning to Rank | BUAA | ICDE | 2021 | CS-F-LTR[^CS-F-LTR] | [[PUB](https://ieeexplore.ieee.org/document/9458704)] [[RELATED PAPER(ZH)](ZH)] |
+| An Efficient Approach for Cross-Silo Federated Learning to Rank | BUAA | ICDE | 2021 | CS-F-LTR[^CS-F-LTR] | [[PUB](https://ieeexplore.ieee.org/document/9458704)] [[RELATED PAPER(ZH)](https://kns.cnki.net/kcms/detail/detail.aspx?doi=10.13328/j.cnki.jos.006174)] |
 | Feature Inference Attack on Model Predictions in Vertical Federated Learning | NUS | ICDE | 2021 | FIA[^FIA] | [[PUB](https://ieeexplore.ieee.org/document/9458672/)] [[PDF](https://arxiv.org/abs/2010.10152)] [[CODE](https://github.com/xj231/featureinference-vfl)] |
 | Efficient Federated-Learning Model Debugging | USTC | ICDE | 2021 | FLDebugger[^FLDebugger] | [[PUB](https://ieeexplore.ieee.org/document/9458829)] |
 | Federated Matrix Factorization with Privacy Guarantee | Purdue | VLDB | 2021 | FMFPG[^FMFPG] | [[PUB](https://www.vldb.org/pvldb/vol15/p900-li.pdf)] |
@@ -943,7 +943,7 @@ In this section, we will summarize Federated Learning papers accepted by top Dat
 |Platform                                                        |    Papers                                                          |    Affiliations                                                    |                     SG                     |                     ST                     |    Materials|
 | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------ | ------------------------------------ | ------------------------------------------------------------ |
 | [PySyft](https://github.com/OpenMined/PySyft)<br />[![Stars](https://img.shields.io/github/stars/OpenMined/PySyft.svg?color=red)](https://github.com/OpenMined/PySyft/stargazers)<br />![](https://img.shields.io/github/last-commit/OpenMined/PySyft) | [A generic framework for privacy preserving deep learning](https://arxiv.org/abs/1811.04017) | [OpenMined](https://www.openmined.org/) |  |  | [[DOC](https://pysyft.readthedocs.io/en/latest/installing.html)] |
-| [FATE](https://github.com/FederatedAI/FATE)<br />[![Stars](https://img.shields.io/github/stars/FederatedAI/FATE.svg?color=red)](https://github.com/FederatedAI/FATE/stargazers)<br />![](https://img.shields.io/github/last-commit/FederatedAI/FATE) | [FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection](https://www.jmlr.org/papers/volume22/20-815/20-815.pdf) | [WeBank](https://fedai.org/) |  | :white_check_mark::white_check_mark: | [[DOC](https://fate.readthedocs.io/en/latest/)] [[DOC(ZH)](ZH)] |
+| [FATE](https://github.com/FederatedAI/FATE)<br />[![Stars](https://img.shields.io/github/stars/FederatedAI/FATE.svg?color=red)](https://github.com/FederatedAI/FATE/stargazers)<br />![](https://img.shields.io/github/last-commit/FederatedAI/FATE) | [FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection](https://www.jmlr.org/papers/volume22/20-815/20-815.pdf) | [WeBank](https://fedai.org/) |  | :white_check_mark::white_check_mark: | [[DOC](https://fate.readthedocs.io/en/latest/)] [[DOC(ZH)](https://fate.readthedocs.io/en/latest/zh/)] |
 | [MindSpore Federated](https://github.com/mindspore-ai/mindspore/tree/master/tests/st/fl)<br />[![Stars](https://img.shields.io/github/stars/mindspore-ai/mindspore.svg?color=red)](https://github.com/mindspore-ai/mindspore/stargazers)<br />![](https://img.shields.io/github/last-commit/mindspore-ai/mindspore) |  | HUAWEI |  |  | [[DOC](https://mindspore.cn/federated/docs/zh-CN/r1.6/index.html)] [[PAGE](https://mindspore.cn/federated)] |
 | [TFF(Tensorflow-Federated)](https://github.com/tensorflow/federated) <br />[![Stars](https://img.shields.io/github/stars/tensorflow/federated.svg?color=red)](https://github.com/tensorflow/federated/stargazers)<br />![](https://img.shields.io/github/last-commit/tensorflow/federated) | [Towards Federated Learning at Scale: System Design](https://arxiv.org/abs/1902.01046) | Google |  |  | [[DOC](https://www.tensorflow.org/federated)] [[PAGE](https://www.tensorflow.org/federated)] |
 | [FedML](https://github.com/FedML-AI/FedML)<br />[![Stars](https://img.shields.io/github/stars/FedML-AI/FedML.svg?color=red)](https://github.com/FedML-AI/FedML/stargazers)<br />![](https://img.shields.io/github/last-commit/FedML-AI/FedML) | [FedML: A Research Library and Benchmark for Federated Machine Learning](https://arxiv.org/abs/2007.13518) | [FedML](https://fedml.ai/) | :white_check_mark::white_check_mark: | :white_check_mark: | [[DOC](https://doc.fedml.ai/)] |
@@ -958,7 +958,7 @@ In this section, we will summarize Federated Learning papers accepted by top Dat
 | [IBM Federated Learning](https://github.com/IBM/federated-learning-lib)<br />[![Stars](https://img.shields.io/github/stars/IBM/federated-learning-lib.svg?color=blue)](https://github.com/IBM/federated-learning-lib/stargazers)<br />![](https://img.shields.io/github/last-commit/IBM/federated-learning-lib) | [IBM Federated Learning: an Enterprise Framework White Paper](https://arxiv.org/abs/2007.10987.pdf) | [IBM](https://github.com/IBM) |  | :white_check_mark: | [[PAPERS](https://github.com/IBM/federated-learning-lib/blob/main/docs/papers.md)] |
 | [KubeFATE](https://github.com/FederatedAI/KubeFATE)<br />[![Stars](https://img.shields.io/github/stars/FederatedAI/KubeFATE.svg?color=blue)](https://github.com/FederatedAI/KubeFATE/stargazers)<br />![](https://img.shields.io/github/last-commit/FederatedAI/KubeFATE) |  | [WeBank](https://fedai.org/) |  |  | [[WIKI](https://github.com/FederatedAI/KubeFATE/wiki/#faqs)] |
 | [Privacy Meter](https://github.com/privacytrustlab/ml_privacy_meter)<br />[![Stars](https://img.shields.io/github/stars/privacytrustlab/ml_privacy_meter.svg?color=blue)](https://github.com/PaddlePaddle/privacytrustlab/ml_privacy_meter)<br />![](https://img.shields.io/github/last-commit/privacytrustlab/ml_privacy_meter) | [Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning](https://ieeexplore.ieee.org/document/8835245) | University of Massachusetts Amherst |  |  |  |
-| [Fedlab](https://github.com/SMILELab-FL/FedLab)<br />[![Stars](https://img.shields.io/github/stars/SMILELab-FL/FedLab.svg?color=blue)](https://github.com/SMILELab-FL/FedLab/stargazers)<br />![](https://img.shields.io/github/last-commit/SMILELab-FL/FedLab) | [FedLab: A Flexible Federated Learning Framework](https://arxiv.org/abs/2107.11621) | [SMILELab](https://github.com/SMILELab-FL/) |  |  | [[DOC](https://fedlab.readthedocs.io/en/master/)] [[DOC(ZH)](ZH)] [[PAGE](https://github.com/SMILELab-FL/FedLab-benchmarks)] |
+| [Fedlab](https://github.com/SMILELab-FL/FedLab)<br />[![Stars](https://img.shields.io/github/stars/SMILELab-FL/FedLab.svg?color=blue)](https://github.com/SMILELab-FL/FedLab/stargazers)<br />![](https://img.shields.io/github/last-commit/SMILELab-FL/FedLab) | [FedLab: A Flexible Federated Learning Framework](https://arxiv.org/abs/2107.11621) | [SMILELab](https://github.com/SMILELab-FL/) |  |  | [[DOC](https://fedlab.readthedocs.io/en/master/)] [[DOC(ZH)](https://fedlab.readthedocs.io/zh_CN/latest/)] [[PAGE](https://github.com/SMILELab-FL/FedLab-benchmarks)] |
 | [Differentially Private Federated Learning: A Client-level Perspective](https://github.com/SAP-samples/machine-learning-diff-private-federated-learning) <br />[![Stars](https://img.shields.io/github/stars/SAP-samples/machine-learning-diff-private-federated-learning.svg?color=blue)](https://github.comSAP-samples/machine-learning-diff-private-federated-learning/stargazers)<br />![](https://img.shields.io/github/last-commit/SAP-samples/machine-learning-diff-private-federated-learning) | [Differentially Private Federated Learning: A Client Level Perspective](https://arxiv.org/abs/1712.07557) | [SAP-samples](https://github.com/SAP-samples) |  |  |  |
 | [NVFlare](https://github.com/NVIDIA/NVFlare)<br />[![Stars](https://img.shields.io/github/stars/NVIDIA/NVFlare.svg?color=blue)](https://github.com/NVIDIA/NVFlare/stargazers)<br />![](https://img.shields.io/github/last-commit/NVIDIA/NVFlare) |  | [NVIDIA](https://github.com/NVIDIA) |  |  | [[DOC](https://nvflare.readthedocs.io/en/2.1.1/)] |
 | [easyFL](https://github.com/WwZzz/easyFL)<br />[![Stars](https://img.shields.io/github/stars/WwZzz/easyFL.svg?color=blue)](https://github.com/WwZzz/easyFL/stargazers)<br />![](https://img.shields.io/github/last-commit/WwZzz/easyFL) | [Federated Learning with Fair Averaging](https://www.ijcai.org/proceedings/2021/223) | XMU |  |  |  |