Release v1.4.5

By downloading, installing or using the software, you accept and agree to be bound by all of the terms and conditions of the LICENSE and DISCLAIMER.

Major Features and Improvements

EggRoll

• RollSite supports the communication certificates

Release v1.4.4

By downloading, installing or using the software, you accept and agree to be bound by all of the terms and conditions of the LICENSE and DISCLAIMER.

Major Features and Improvements

FATE-Flow

• Task Executor supports monkey patch
• Add forward API

Release v1.4.3

By downloading, installing or using the software, you accept and agree to be bound by all of the terms and conditions of the LICENSE and DISCLAIMER.

Major Features and Improvements

FederatedML

• Fix bug of Hetero SecureBoost of sending tree weight info from guest to host.

Release v1.4.2

By downloading, installing or using the software, you accept and agree to be bound by all of the terms and conditions of the LICENSE and DISCLAIMER.

Major Features and Improvements

FederatedML

• Optimize performance of Pearson which increases efficiency by more than twice.
• Optimize Min-test module: Add secure-boost as optional test task. Set partyid and work_mode as input parameters. Use pre-import data set as input so that improved test process.
• Support tok_k iv filter in feature selection module.
• Support filling missing value for tag:value format data in DataIO.
• Fix bug of lacking one layer of depth of tree in HeteroSecureBoost and support automatically alignment header of input data in predict process
• Standardize the naming of example data set and add a data pre-import script.

FATE-Flow

• Distinguish between user stop job and system stop job;
• Optimized some logs;
• Optimize zookeeper configuration
• The model supports persistent storage to mysql
• Push the model to the online service to support the specified storage address (local file and FATEFlowServer interface)

Release 1.4.1

By downloading, installing or using the software, you accept and agree to be bound by all of the terms and conditions of the LICENSE and DISCLAIMER.

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

By downloading, installing or using the software, you accept and agree to be bound by all of the terms and conditions of the LICENSE and DISCLAIMER.

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 1.2.2

1. fix union component bug while only has one input
2. fix union component bug while input table is empty

Release 1.3.0

By downloading, installing or using the software, you accept and agree to be bound by all of the terms and conditions of the LICENSE and DISCLAIMER.

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 1.2.1

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