Release 1.4.1

Major Features and Improvements

FederatedML

• Reconstructed Evaluation Module improves efficiency by 60 times
• Add PSI, confusion matrix, f1-score and quantile threshold support for Precision/Recall in Evaluation.
• Add option to retain duplicated keys in Union.
• Support filter feature based on mode
• Manual filter allows manually set columns to retain
• Auto recoginize whether a data set includes a label column in predict process
• Bug-fix: Missing schema after merge in Union; Fail to align label of multi-class in homo_nn with PyTorch backend; Floating-point precision error and value error due to int-type input in Feature Scale

FATE-Flow

• Allow the host to stop the job
• Optimize the task queue
• Automatically align the input table partitions of all participants when the job is running
• Fate flow client large file upload optimization
• Fixed some bugs with abnormal status

Release 1.4.0

Major Features and Improvements

FederatedML

• Support Homo Secureboost
• Support AIC/BIC-based Stepwise for Linear Models
• Add Hetero Optimal Feature Binning, support iv/gini/chi-square/ks,and allow asymmetric binning methods
• Interoperate with AI ecosystem: Add pytorch backend for Homo NN
• Homo Framework factorization, simplify developing homo algorithms
• Early stopping strategy for hetero algorithms.
• Local Baseline supports multi-class classification
• Add consistency check to Predict function
• Optimize validation strategy，3x speed up in-training validation

FATE-Flow

• Refactoring model management, native file directory storage, storage structure is more flexible, more information
• Support model import and export, store and restore with reliable distributed system(Redis is currently supported)
• Using MySQL instead of Redis to implement Job Queue, reducing system complexity
• Support for uploading client local files
• Automatically detects the existence of the table and provides the destroy option
• Separate system, algorithm, scheduling command log, scheduling command log can be independently audited

Eggroll

Stability Boosts:

• New resource management components introduce the brand new session mechanism. Processors can be cleaned up with a simple method call, even the session goes wrong.
• Removes storage service. No C++ / native library compilation is needed.
• Federated learning algorithms can still work at a 28% packet loss rate.

Performance Boosts:

• Performances of federated learning algorithms are improved on Eggroll 2. Some algorithms get 10x performance boost.
• Join interface is 16x faster than pyspark under federated learning scenarios.

User Experiences Boosts:

• Quick deployment. Maven, pip, config and start.
• Light dependencies. Check our requirements.txt / pom.xml and see.
• Easy debugging. Necessary running contexts are provided. Runtime status are kept in log files and databases.
• Few daemon processes. And they are all JVM applications.

Release 1.3.1

Major Features and Improvements

Deploy

• Support deploying by MacOS
• Support using external db
• Deploy JDK and Python environments on demand
• Improve MySQL and FATE Flow service.sh
• Support more custom deployment configurations in the default_configurations.sh, such as ssh_port, mysql_port and so one.

Release 1.3.0

Major Features and Improvements

FederatedREC

• Add federated recommendation submodule
• Add heterogeneous Factoraization Machine
• Add hemogeneous Factoraization Machine
• Add heterogeneous Matrix Factorization
• Add heterogeneous Singular Value Decomposition
• Add heterogeneous SVD++ (Factorization Meets the Neighborhood)
• Add heterogeneous Generalized Matrix Factorization

FederatedML

• Support Sparse data training in heterogeneous General Linear Model(Hetero-LR、Hetero-LinR、Hetero-PoissonR)
• Fix 32M limitation of quantile binning to support higher feature dimension
• Fix 32M limitation of histogram statistics for SecureBoost to support higher feature dimension training.
• Add abnormal parameters and input data detection in OneHot Encoder
• fix not passing validate data to fit process to support evaluate validation data during training process

FATE-Flow

• Add clean job CLI for cleaning output and intermediate results, including data, metrics and sessions
• Support for obtaining table namespace and name of output data via CLI
• Fix KillJob unsuccessful execution in some special cases
• Improve log system, add more exception and run time status prompts

Release 1.2.0

Major Features and Improvements

FederatedML

• Add heterogeneous Deep Neural Network
• Add Secret-Sharing Protocol-SPDZ
• Add heterogeneous feature correlation algorithm with SPDZ and support heterogeneous Pearson Correlation Calculation
• Add heterogeneous SQN optimizer, available for Hetero-LogisticRegression and Hetero-LinearRegression, which can reduce communication costs significantly
• Supports intersection for expanding duplicate IDs
• Support multi-host in heterogeneous feature binning
• Support multi-host in heterogeneous feature selection
• Support IV calculation for categorical features in heterogeneous feature binning
• Support transform raw feature value to WOE in heterogeneous feature binning
• Add manual filters in heterogeneous feature selection
• Support performance comparison with sklearn's logistic regression
• Automatic object/table clean in training iteration procedure in Federation
• Improve transfer performance for large object
• Add automatic scripts for submitting and running tasks

FATE-Flow

• Add data management module for recording the uploaded data tables and the outputs of the model in the job running, and for querying and cleaning up CLI.
• Support registration center for simplifying communication configuration between FATEFlow and FATEServing
• Restruct model release logic, FATE_Flow pushes model directly to FATE-Serving. Decouple FATE-Serving and Eggroll, and the offline and online architectures are connected only by FATE-Flow.
• Provide CLI to query data upload record
• Upload and download data support progress statistics by line
• Add some abnormal diagnosis tips
• Support adding note information to job

Native Deploy

• Fix bugs in EggRoll startup script, add mysql, redis startup options.
• Disable host name resolution configuration for mysql service.
• The version number of each module of the software packaging script is updated using the automatic acquisition mode.

Release 1.1.1

Major Features and Improvements

• Add cluster deployment support based on ubuntu operating system。
• Add union component which support data merging.
• Support indicating partial columns in Onehot Encoder
• Support intermediate data cleanup after the task ends
• Accelerated Intersection
• Optimizing the deployment process

Bug Fixes

• Fix a bug of secureboost' early stop
• Fix a bug in download api
• Fix bugs of spark-backend

Release 1.1

Major Features and Improvements

FederatedML

• 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

FATE-Flow

• 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
• Add authentication control Beta version, including component, command, role

Release 1.0.2

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 of multi-host support in Cross-Validation
• Fix bugs of showing up evaluation metrics when both train and eval exist
• Add links for each algorithm module in FederatedML home page README

Release 1.0.1

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

Release 1.0

Major Features and Improvements

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.

FATE-Board

• Federated Learning Job DashBoard
• Federated Learning Job Visualisation
• Federated Learning Job Management
• Real-time Log Panel

FATE-FLOW

• DAG defines 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

FederatedML

• Update all algorithm modules running mechanism for supporting federated modeling pipeline by FATE-Flow
• Intermediate statistic result callback is available and visualizable in FATE-Board for all algorithm modules.
• Support Nesterov Momentum SGD Optimizer
• Add Homomorphic Encryption Scheme Based on Affine Transforms
• Support sparse input-format in federated feature binning
• Update evaluation metrics, such as ks, roc, gain, lift curve and so on
• Update algorithm's parameter-define class

FATE-Serving

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

Release 0.3.2

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

Release 0.3.1

Bug Fixes

• fix feature scale bugs in v0.3
• fix federated feature selection bugs in v0.3

Release 0.3

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

Bug Fixes and Other Changes

• fix detect onehot max column overflow bug.
• fix dataio dense format not reading host data header bug.
• fix bugs of call of statistics function
• fix bug for federated feature selection that at least one feature remains for each party
• Not allowing so small batch size in LR module for safety consideration.
• fix naming error in federated feature selection module.
• Fix the bug of automated publishing model information in some extreme cases
• Fixed some overflow bugs in fixed-point data
• fix many other bugs.

Release 0.2

Major Features and Improvements

WorkFlow

• 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

FederatedML

• 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

FATE-Serving(An online inference for federated learning models)

• 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

EggRoll

• Add Node Manager for multiprocessor to improve distributed computing performance
• Add C++ overwrite storage service
• Add eggroll cleanup API

Deploy

• Add auto-deploy
• Improved deployment documentation

Example

• 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

Release 0.1

Initial release of FATE.

Major Features

WorkFlow

• Support Intersection workflow
• Support Train workflow
• Support Predict workflow
• Support Validation workflow
• Support Model Load and Save workflow

FederatedML

• 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

Architecture

• 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

Deploy

• Support standalone (docker & manual) deployment
• Support cluster deployment