Release Note of FATE

Major Features and Improvements


* Add union component which support data merging.

* Support indicating partial columns in Onehot Encoder

* Fix a bug of secureboost’ early stop


* Support indicating partial columns in Onehot Encoder

> Fate-Flow

* Fix bugs in download api

* Supports intermediate data cleanup after the task ends


* Add Union component and support the model visualization


* Added cluster deployment support based on ubuntu operating system

Major Features and Improvements


* Provide a general algorithm framework for homogeneous federated learning, which supports Secure Aggregation

* Add Homogeneous Deep Neural Network

* Add Heterogeneous Linear Regression

* Add Heterogeneous Poisson Regression

* Support multi-host in Heterogeneous Logistic Regression

* Support multi-host in Heterogeneous Linear Regression

* Support multi-host Intersection

* Accelerated Intersection by usage of cache

* Reconstruct heterogeneous Generalized Linear Models Framework

* Support affine homomorphic encryption in Heterogeneous SecureBoost

* Support input data with missing value in Heterogeneous SecureBoost

* Support evaluation during training on both train and validate data

* Add spark as computing engine



* Upload and Download support CLI for querying job status

* Support for canceling waiting job

* Support for setting job timeout

* Support for storing a job scheduling log in the job log folder



* Support the visual tree models of Heterogeneous Secureboost

* Support separation of data ports and model ports in all the components

* Support visualization of evaluation results during training

* Support for job queries, filters, and list sorting

* Optimize dashboard and workflow visualization,and support workflow scaling



* Add Online OneHotEncoder transform

* Add Online Heterogeneous FeatureBinning transform

* Add heterogeneous SecureBoost Online Inference

* Add service governance

* Support automatically to restore the loaded model when service restarts



* Deployment using Docker Compose

* Helm Charts for Kubernetes

* Integration of Harbor container image warehouse

Major Features and Improvements

* Python and JDK environment are required only for running standalone version quick experiment

* Support cluster version docker deployment

* Add deployment guide in Chinese

* Standalone version job for quick experiment is supported when cluster version deployed.

* Python service log will remain for 14 days now.

Bug Fixes

* Fix bugs for evaluation data type

* Fix bugs for feature binning to take abnormal values into consideration

* Fix bugs for train and eval

* Fix bugs in binning merge

* Fix bugs in Samplers

* Fix federated feature selection feature filter bug

* Support upload file in version argument

* Support get serviceRoleName from configuration

This version includes two new products of FATE, FATE-Board, and FATE-Flow respectively, FATE-Board as a visual tool for federation modeling, and FATE-Flow is an end to end pipeline platform for federated learning. This version contains important improvements to the FederatedML, which better tracks the running progress of federated learning algorithms.

New features


* DAG define Pipeline

* Federated Multi-party asymmetric DSL parser

* Federated Learning lifecycle management

* Federated Task collaborative scheduling

* Tracking for data, metric, model and so on

* Federated Multi-party model management



* Federated Learning Job DashBoard

* Federated Learning Job Visualisation

* Federated Learning Job Management

* Real-time Log Panel


Important upgrade


* Update all algorithm modules running mechanism. Support federated modeling pipeline by FATE-Flow.

* Intermediate statistic result callback is available and visualizable in FATE-Board for all algorithm modules.

* Support sparse input-format in federated feature binning

* Update evaluation metrics result

* Update algorithm’s parameter-define class



* Add online federated modeling pipeline DSL parser for online federated inference

Bug Fixes

* Adjust the Logic of Online Service Module

* Adjust the log format

* Replace the grpc connection pool of the online service module

* Improving Model Processing Details

Bug Fixes

* fix feature scale bugs in v0.3

* fix federated feature selection bugs in v0.3

Major Features and Improvements

> FederatedML

* Support OneVsALL for multi-label classification task

* Add trash-recycle in Hetero Logistic Regression

* Add numeric stable for sigmoid and log_logistic function.

* Support different calculation mode in Hetero Logistic Regression and Hetero SecureBoost

* Decouple Federated Feature Binning and Federated Feature Selection

* Add feature importance calculation in Hetero SecureBoost

* Add multi-host in Hetero SecureBoost

* Support tag:value sparse format input data

* Support output intersect-id with feature-instance in Intersection

* Support OneHot encoding module.

* Support bucket binning for Federated Feature Binning.

* Support add, sub, mul, div ,gt, lt ,eq, etc mathematical operator on Fixed-Point data

* Add authority validation for parameter setting


> FATE-Serving

* Add multi-level cache for multi-party inference result

* Add startInferceJob and getInferenceResult interfaces to support the inference process asynchronization

* Normalized inference return code

* Real-time logging of inference summary logs and inferential detail logs

* Improve the loading of the pre and post processing adapter and data access adapter for host


> EggRoll

* New computing and storage APIs

* Stability optimizations

* Performance optimizations

* Storage usage improvements


> Example

* Add Mini-FederatedML test task example

* Using task manager to submit distributed task for current examples

Major Features and Improvements


* Add Model PipleLine

* Add Hetero Federated Feature Binning workflow

* Add Hetero Federated Feature Selection workflow

* Add hetero dnn workflow

* Add intersection operator before train, predict and cross_validation



* Support svm-light sparse format inputdata

* Support tag sparse format inputdata

* Add Hetero Federated Feature Binning

* Add Hetero Federated Feature Selection

* Add Feature Scaler: MinMaxScaler & StandardScaler

* Add Feature Imputer for missing value filling

* Add Data Statistic for datainstance

* Support encoding and main calculation role configurable for RAW Intesection

* Add Sampler: RandomSampler & StratifiedSampler

* Support regression in SecureBoost

* Support regression evaluation

* Support Decentralized FTL

* Add feature extracting by DNN

* Change Model Format to ProtoBuf

* Add abnormal parameter detection

* Add abnormal input data detection



* Dynamic Loading Federated Learning Models

* Real-time Prediction Using Federated Learning Models


>Model Management

* Versioning

* Reproducibility

* Queries, Search


>Task Manager

* Add Load File/ Download File

* Add Import ID from Local File

* Add Start workflow

* Add workflow Job Queue

* Add Query Job Status

* Add Get Runtime conf

* Add Delete Task



* Add Node Manager for multiprocessor to improve distributed computing performance

* Add C++ overwrite storage service

* Add eggroll cleanup API


* Add auto-deploy

* Improved deployment documentation



* Add Hetero Federated Feature Binning example

* Add Hetero Federated Feature Selection example

* Add Hetero DNN example

* Add toy example

* Add task manager examples

* Add Serving example


Bug Fixes and Other Changes

* Hetero-LR Minibath bugfixed

* Gradient Average bugfixed

* One-second latency for proxy bugfixed

* Training flowid bugfixed

* Many bugfixes

* Many performance improvements

* Many documentation fixes

Major Features


* Support Intersection workflow

* Support Train workflow

* Support Predict workflow

* Support Validation workflow

* Support Model Load and Save workflow


* Support Distributed Secure Intersection and Raw Intersection for Sample Alignment

* Support Distributed Homogeneous LR and Heterogeneous LR

* Support Distributed SecureBoost

* Support Distributed Secure Federated Transfer Learning

* Support Binary and Multi-Class Evaluation

* Support Model Cross-Validation

* Supprt Mini-Batch

* Support L1, L2 Regularizers

* Support Multi-Party Homogeneous FederatedAggregator

* Support Multi-Party Heterogeneous FederatedAggregator

* Support Partially Homomorphic Encryption MPC Protocol


* Initial release of Computing APIs

* Initial release of Storage APIs

* Initial release of Federation APIs

* Initial release of cross-site network communication (i.e. ‘Federation’)

* Initial release of Standalone runtime, including computing engine and k-v storage

* Initial release of Distributed runtime, including distributed computing engine, distributed k-v storage, metadata management and intra-site/cross-site network communication

* Support cross-site heterogenous infrastructure

* Initial support of modeling and inference


* Support standalone (docker & manual) deployment

* Support cluster deployment