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README.md | 1 vuosi sitten | |
README.zh.md | 1 vuosi sitten | |
column_expand.md | 1 vuosi sitten | |
correlation.md | 1 vuosi sitten | |
data_split.md | 1 vuosi sitten | |
data_transform.md | 1 vuosi sitten | |
ensemble.md | 1 vuosi sitten | |
evaluation.md | 1 vuosi sitten | |
feature_binning.md | 1 vuosi sitten | |
feature_imputation.md | 1 vuosi sitten | |
feature_selection.md | 1 vuosi sitten | |
feldman_verifiable_sum.md | 1 vuosi sitten | |
hetero_ftl.md | 1 vuosi sitten | |
hetero_kmeans.md | 1 vuosi sitten | |
hetero_nn.md | 1 vuosi sitten | |
homo_framework.md | 1 vuosi sitten | |
homo_nn.md | 1 vuosi sitten | |
homo_onehot_encoder.md | 1 vuosi sitten | |
intersect.md | 1 vuosi sitten | |
label_transform.md | 1 vuosi sitten | |
linear_model_gradient.md | 1 vuosi sitten | |
linear_regression.md | 1 vuosi sitten | |
local_baseline.md | 1 vuosi sitten | |
logistic_regression.md | 1 vuosi sitten | |
onehot_encoder.md | 1 vuosi sitten | |
poisson_regression.md | 1 vuosi sitten | |
positive_unlabeled.md | 1 vuosi sitten | |
psi.md | 1 vuosi sitten | |
sample.md | 1 vuosi sitten | |
sample_weight.md | 1 vuosi sitten | |
scale.md | 1 vuosi sitten | |
scorecard.md | 1 vuosi sitten | |
secureprotol.md | 1 vuosi sitten | |
sir.md | 1 vuosi sitten | |
statistic.md | 1 vuosi sitten | |
stepwise.md | 1 vuosi sitten | |
union.md | 1 vuosi sitten |
[中文]
FederatedML includes implementation of many common machine learning algorithms on federated learning. All modules are developed in a decoupling modular approach to enhance scalability. Specifically, we provide:
Algorithm | Module Name | Description | Data Input | Data Output | Model Input | Model Output |
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DataTransform | DataTransform | This component transforms user-uploaded data into Instance object. | Table, values are raw data. | Transformed Table, values are data instance defined here | DataTransform Model | |
Intersect | Intersection | Compute intersect data set of multiple parties without leakage of difference set information. Mainly used in hetero scenario task. | Table. | Table with only common instance keys. | Intersect Model | |
Federated Sampling | FederatedSample | Federated Sampling data so that its distribution become balance in each party.This module supports standalone and federated versions. | Table | Table of sampled data; both random and stratified sampling methods are supported. | ||
Feature Scale | FeatureScale | module for feature scaling and standardization. | Table,values are instances. | Transformed Table. | Transform factors like min/max, mean/std. | |
Hetero Feature Binning | HeteroFeatureBinning | With binning input data, calculates each column's iv and woe and transform data according to the binned information. | Table, values are instances. | Transformed Table. | iv/woe, split points, event count, non-event count etc. of each column. | |
Homo Feature Binning | HomoFeatureBinning | Calculate quantile binning through multiple parties | Table | Transformed Table | Split points of each column | |
OneHot Encoder | OneHotEncoder | Transfer a column into one-hot format. | Table, values are instances. | Transformed Table with new header. | Feature-name mapping between original header and new header. | |
Hetero Feature Selection | HeteroFeatureSelection | Provide 5 types of filters. Each filters can select columns according to user config | Table | Transformed Table with new header and filtered data instance. | If iv filters used, hetero_binning model is needed. | Whether each column is filtered. |
Union | Union | Combine multiple data tables into one. | Tables. | Table with combined values from input Tables. | ||
Hetero-LR | HeteroLR | Build hetero logistic regression model through multiple parties. | Table, values are instances | Table, values are instances. | Logistic Regression Model, consists of model-meta and model-param. | |
Local Baseline | LocalBaseline | Wrapper that runs sklearn(scikit-learn) Logistic Regression model with local data. | Table, values are instances. | Table, values are instances. | ||
Hetero-LinR | HeteroLinR | Build hetero linear regression model through multiple parties. | Table, values are instances. | Table, values are instances. | Linear Regression Model, consists of model-meta and model-param. | |
Hetero-Poisson | HeteroPoisson | Build hetero poisson regression model through multiple parties. | Table, values are instances. | Table, values are instances. | Poisson Regression Model, consists of model-meta and model-param. | |
Homo-LR | HomoLR | Build homo logistic regression model through multiple parties. | Table, values are instances. | Table, values are instances. | Logistic Regression Model, consists of model-meta and model-param. | |
Homo-NN | HomoNN | Build homo neural network model through multiple parties. | Table, values are instances. | Table, values are instances. | Neural Network Model, consists of model-meta and model-param. | |
Hetero Secure Boosting | HeteroSecureBoost | Build hetero secure boosting model through multiple parties | Table, values are instances. | Table, values are instances. | SecureBoost Model, consists of model-meta and model-param. | |
Hetero Fast Secure Boosting | HeteroFastSecureBoost | Build hetero secure boosting model through multiple parties in layered/mix manners. | Table, values are instances. | Table, values are instances. | FastSecureBoost Model, consists of model-meta and model-param. | |
Evaluation | Evaluation | Output the model evaluation metrics for user. | Table(s), values are instances. | |||
Hetero Pearson | HeteroPearson | Calculate hetero correlation of features from different parties. | Table, values are instances. | |||
Hetero-NN | HeteroNN | Build hetero neural network model. | Table, values are instances. | Table, values are instances. | Hetero Neural Network Model, consists of model-meta and model-param. | |
Homo Secure Boosting | HomoSecureBoost | Build homo secure boosting model through multiple parties | Table, values are instances. | Table, values are instances. | SecureBoost Model, consists of model-meta and model-param. | |
Homo OneHot Encoder | HomoOneHotEncoder | Build homo onehot encoder model through multiple parties. | Table, values are instances. | Transformed Table with new header. | Feature-name mapping between original header and new header. | |
Hetero Data Split | HeteroDataSplit | Split one data table into 3 tables by given ratio or count | Table, values are instances. | 3 Tables, values are instance. | ||
Homo Data Split | HomoDataSplit | Split one data table into 3 tables by given ratio or count | Table, values are instances. | 3 Tables, values are instance. | ||
Column Expand | ColumnExpand | Add arbitrary number of columns with user-provided values. | Table, values are raw data. | Transformed Table with added column(s) and new header. | Column Expand Model | |
Secure Information Retrieval | SecureInformationRetrieval | Securely retrieves information from host through oblivious transfer | Table, values are instance | Table, values are instance | ||
Hetero Federated Transfer Learning | FTL | Build Hetero FTL Model Between 2 party | Table, values are instance | Hetero FTL Model | ||
Hetero KMeans | HeteroKMeans | Build Hetero KMeans model through multiple parties | Table, values are instance | Table, values are instance; Arbier outputs 2 Tables | Hetero KMeans Model | |
PSI | PSI | Compute PSI value of features between two table | Table, values are instance | PSI Results | ||
Data Statistics | DataStatistics | This component will do some statistical work on the data, including statistical mean, maximum and minimum, median, etc. | Table, values are instance | Table | Statistic Result | |
Scorecard | Scorecard | Scale predict score to credit score by given scaling parameters | Table, values are predict score | Table, values are score results | ||
Sample Weight | SampleWeight | Assign weight to instances according to user-specified parameters | Table, values are instance | Table, values are weighted instance | SampleWeight Model | |
Feldman Verifiable Sum | FeldmanVerifiableSum | This component will sum multiple privacy values without exposing data | Table, values to sum | Table, values are sum results | ||
Feature Imputation | FeatureImputation | This component imputes missing features using arbitrary methods/values | Table, values are Instances | Table, values with missing features filled | FeatureImputation Model | |
Label Transform | LabelTransform | Replaces label values of input data instances and predict results | Table, values are Instances or prediction results | Table, values with transformed label values | LabelTransform Model | |
Hetero SSHE Logistic Regression | HeteroSSHELR | Build hetero logistic regression model without arbiter | Table, values are Instances | Table, values are Instances | SSHE LR Model | |
Hetero SSHE Linear Regression | HeteroSSHELinR | Build hetero linear regression model without arbiter | Table, values are Instances | Table, values are Instances | SSHE LinR Model | |
Positive Unlabeled Learning | PositiveUnlabeled | Build positive unlabeled learning model | Table, values are Instances | Table, values are Instances |