Release v2.0.0-alpha

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Feature Highlights

Arch 2.0：Building Unified and Standardized API for Heterogeneous Computing Engines Interconnection

• Introduce Context to manage useful APIs for developers, such as Metrics, Cipher, Tensor and IO.
• Introduce Tensor data structure to handle local and distributed matrix operation, with built-in heterogeneous acceleration support.
• Introduce DataFrame, a 2D tabular data structure for data io and simple feature engineering.
• Refactor logger, customizable logging for different use cases and flavors.
• Introduce new high-level federation API suite: context.<role>.get(name)/context.<role>.put(name=value).

Components 2.0: Building Standardized Algorithm Components for different Scheduling Engines

• Introduce components toolbox to wrap ML modules as standard executable programs.
• spec and loader expose clear API for smooth internal extension and external system integration.
• Provide several cli tools to interact and execute components.
• Implement base demos components: reader, intersection, feature scale, lr and evaluation.

ML 2.0(demo)

• Provide base demos for federated machine learning algorithm: intersection、feature scale、lr and evaluation.

Pipeline 2.0: Building Scalable Federated DSL for Application Layer Interconnection

• Introduce new scalable and standardized federated DSL IR(Intermediate Representation) for federated modeling job
• Compile python client to DSL IR
• Support multiple scalable execution backends, including standalone and Fate-Flow.

OSX(Open Site Exchange) 1.0: Building Open Platform for Cross-Site Communication Interconnection

• Standardized Cross-Site lower-level federation api
• Support grpc synchronous transmission and streaming transmission; Compatible with eggroll interface and can replace FATE-1.x rollsite component
• Support asynchronous message transmission, which can replace rabbitmq and pulsar components in FATE-1.x
• Support HTTP-1.X protocol transmission
• Support cluster deployment and inter-site traffic control
• Support networking as an Exchange component

FATE Flow 2.0: Building Open and Standardized Scheduling Platform for Scheduling Interconnection

• Adapted to new scalable and standardized federated DSL IR
• Standardized API interface with param type checking
• Decoupling Flow from FATE repository
• Optimized scheduling logic, with configurable dispatcher decoupled from initiator
• Support container-level algorithm loading and task scheduling, enhancing support for cross-platform heterogeneous scenarios
• Independent maintenance for system configuration to enhance flexibility and ease of configuration
• Support new communication engine OSX, while compatible with all engines from Flow 1.X
• Introduce OFX(Open Flow Exchange) module: encapsulated scheduling client to allow cross-platform scheduling

Deploy

• Support installing from PyPI

Release v1.10.0

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

• Renewed Homo NN: PyTorch-based, support flexible model building:
  • Support user access to complex self-defined PyTorch models or ready-to-use PyTorch models such as DeepFM, ResNet, BERT, Yolo
  • Support various data set types, may build data set based on PyTorch Dataset
  • User-defined training loss
  • User-defined training process: user-defined aggregation algorithm for client and server
  • Provide API for developing Aggregator
• Upgraded Hetero NN: support flexible model building and various data set types:
  • more flexible pytorch top/bottom model customization; provide access to industry approved PyTorch models
  • User-defined training loss
  • Support various data set types, may build data set based on PyTorch Dataset
• Renewed Homo-federated framework with support for all current homo models, including Homo NN, Homo LR,Homo SecureBoost,
  Homo Feature Binning, and Hetero KMeans. This provides smoother algorithm customization and development experience
• Semi-Supervised Algorithm Positive Unlabeled Learning
• Hetero LR & Hetero SecureBoost now supports Intel IPCL
• Intersection support Multi-host Elliptic-curve-based PSI
• Intersection may compute Multi-host Secure PSI Cardinality
• Hetero Feature Optimal Binning now record & show Gini/KS/Chi-Square metrics
• Host may load Hetero Binning model with WOE score through Model Loader
• Hetero Feature Binning support binning by user-provided split points
• Sampler support weighted sampling by instance weight

Fate-Client

• Flow CLI adds min-test options
• Pipeline adds data-bind API, useful for local development
• Pipeline may reconfigure role/model_id/model_version, switching party_id for prediction task

Release v1.8.1

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Major Features and Improvements

EggRoll

• Support EggRoll v2.4.7

Release v1.7.3

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

EggRoll

• Support EggRoll v2.4.7

Release v1.9.2

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

EggRoll

• Support EggRoll v2.4.7

Release v1.9.1

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

Bug-Fix

• Fix cipher compression with large Hessian value for HeteroSecureBoost
• Fix tweedie-loss calculation in HeteroSecureBoost
• Fix Intersection summary when left-joining data with match_id
• Fix event/non_event statistic for WOE computation in HeteroFeatureBinning
• Fix default sid name display for data uploaded with meta

Release v1.9.0

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

• Add elliptic curve based PSI algorithm, which allows 128-bit secure-level intersection of billion samples, 20x faster than RSA protocol under the same security level
• Support accurate intersection cardinality calculation
• Support for multi-column ID data; user may specify id column for PSI intersection and subsequent modeling usage
• Hetero NN supports torch backend and supports complex layers such as LSTM
• Add CoAE label reinforcement mechanism for vertical federated neural network
• Hetero NN supports multi-host modeling scenarios
• HeteroSecureBoost supports merging sub-models from all parties and exporting the merged model into lightgbm or PMML format
• HeteroLR and HeteroSSHELR support merging sub-models from all parties and exporting the merged model into sklearn or PMML format
• HeteroFeatureSelection supports anonymous feature selection
• Label Encoder adds automatic label type inference
• 10x faster local VIF computation in HeteroPearson, with added support for computing local VIF on linearly dependent columns
• Optimized feature engineering column processing logic
• HeteroFeatureBinning supports calculation of IV and WOE values during prediction
• Renewed feature anonymous generation logic

FATE-ARCH

• Support python3.8+
• Support Spark 3x
• Renewed Federation module, RabbitMQ and Pulsar support client transmission mode
• Support Standalone, Spark, EggRoll heterogeneous computing engine interconnection

Fate-Client

• PipeLine adds timeout retry mechanism
• Pipeline's get_output_data API now may return component output data in DataFrame-typed format

Release v1.8.0

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

• Add non-coordinated-version Hetero Linear Regression, based on integrated Hetero GLM framework, with mixed protocol of HE and SPDZ
• Homo LR support one-vs-rest
• Add SecureBoost-MO algorithm to speed up multi-class classification of Hetero & Homo SecureBoost, 1.5x-5x faster
• Optimize Hetero SecureBoost Predict Transmission Data Size，reduce 75% bandwidth consumption if tree's max depth is small
• Speed up DH Intersection implementation, 30%+ faster
• Optimized Quantile Binning gk-summary structure & split point query，20%+ faster, less memory cost
• Support weighted training in non-coordinated Hetero Logistic Regression & Linear Regression
• Merge Hetero FastSecureBoost into Hetero SecureBoost as a boosting strategy option

Fate-ARCH

• Adjustable task_cores for standalone FATE
• Enable Eggroll option to make computing output "IN_MEMORY" by default

Fate-Test

• Include Paillier encryption performance evaluation
• Include SPDZ performance evaluation
• Optimized testsuite printout
• Include examples data upload and mnist download
• Provide pipeline to dsl convert tools

Bug-Fix

• Fix bug for SPDZ when using default q_filed
• Fix multiple get problem of SPDZ
• Fix bugs of recursive-query homo feature binning
• Fix homo_nn's model aggregation problem
• Fix bug for hetero feature selection when using federated filter but some party's feature is empty.

Release v1.5.3

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Major Features and Improvements

FederatedML

• New batch strategy in coordinated Hetero LR: support masked batch data and batch shuffle
• Iterative Affine is disabled

Eggroll

• Support Eggroll v2.2.3, upgrade com.h2database:h2 to version 2.1.210, com.google.protobuf:protobuf-java to version 3.16.1, spring to version 5.1.20.RELEASE

Release v1.7.2

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

• New batch strategy in coordinated Hetero LR: support masked batch data and batch shuffle
• Model inference protection enhancement for Hetero SecureBoost with FED-EINI algorithm
• Hetero SecureBoost supports split feature importance on host side, disables gain feature importance
• Offline SBT Feature transform component

Bug-Fix

• Fixed Bug for HeteroPearson with changing default q_field value for spdz
• Fix Data Transform's schema label name setting problem when with_label is False
• Add testing examples for new algorithm features, and delete deprecated params in algorithm examples.

FATE-ARCH

• Support the loading of custom password encryption modules through plug-ins
• Separate the base connection address of the data storage table from the data table information, and compatible with historical versions