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.
- 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
- Fix a bug of secureboost' early stop
- Fix a bug in download api
- Fix bugs of spark-backend
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
- 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.
- 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
- 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.
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
- 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
- fix feature scale bugs in v0.3
- fix federated feature selection bugs in v0.3
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
- 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.
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
- Hetero-LR Minibath bugfixed
- Gradient Average bugfixed
- One-second latency for proxy bugfixed
- Training flowid bugfixed
- Many bugfixes
- Many performance improvements
- Many documentation fixes
Initial release of FATE.
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