Skip to content

Release v1.7.0
Compare
Choose a tag to compare
@mgqa34 mgqa34 released this 24 Nov 09:37
· 4208 commits to master since this release
v1.7.0
12c2595
This commit was created on GitHub.com and signed with GitHub’s verified signature. The key has expired.
GPG key ID: 4AEE18F83AFDEB23
Expired
Learn about vigilant mode.

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-ARCH

  • Support EggRoll 2.4.0
  • Support Spark-Local Computing Engine
  • Support Hive Storage
  • Support LocalFS Storage for Spark-Local Computing Engine
  • Optimizing the API interface for Storage session and table
  • Simplified the API interface for Session, remove backend and workmode parameters
  • Heterogeneous Engine Support: Federation between Spark-Local and Spark-Cluster
  • Computing Engine, Storage Engine, Federation Engine are set in conf/service_conf.yaml when FATE is deployed

FederatedML

  • Optimized Hetero-SecureBoost: with gradient packing、cipher compressing, and sparse point statistics optimization, 4x+ faster
  • Homo-SecureBoost supports memory-based histogram computation for more efficient tree building, 5x+ faster
  • Optimized RSA Intersect with CRT optimization, 3x+ faster
  • New two-party Hetero Logistic Regression Algorithm: mixed protocol of HE and MPC, without a trusted third party
  • Support data with match-id, separating match id and sample id
  • New DH Intersect based on PH Key-exchange protocol
  • Intersect support cardinality estimation
  • Intersect adds optionally preprocessing step
  • RSA and DH Intersect support cache
  • New Feature Imputation module: can apply arbitrary imputation method to each column
  • New Label Transform module: transform categorical label values
  • Homo-LR, Homo-SecureBoost, Homo-NN now can convert models into sklearn、lightgbm、torch & tf-keras framework
  • Hetero Feature Binning supports multi-class task, higher efficiency with label packing
  • Hetero Feature Selection support multi-class iv filter
  • Secure Information Retrieval supports multi-column retrieval
  • Major training algorithms support warm-start and checkpoint : Homo & Hetero LR, Homo & Hetero-SecureBoost, Homo & Hetero NN
  • Optimized Pailler addition operation, several times faster, Hetero-SecureBoost with Paillier speed up 2x+

Fate-Client

  • Pipeline supports uploading match id functionality
  • Pipeline supports homo model conversion
  • Pipeline supports model push to FATE-Serving
  • Pipeline supports running jobs with specified FATE version

FATE-Test

  • Integrate FederatedML unittest
  • Support for uploading image data
  • Big data generation using storage interface, optimized generation logic
  • Support for historical data comparison
  • cache_deps and model_loader_deps support
  • Run DSL Testsuite with specified FATE version
Assets 2
Loading