## Release 2.2.0 ### Major Features and Improvements > Deployment * Upgrade from Python 3.8 to Python 3.10 * Upgrade the PyTorch version to 2.x ## Release 2.1.1 ### Major Features and Improvements > Component * Support server model saving in Homo-NN > ML * aggregator support aggregation of torch.bfloat16 data type ## Release 2.1.0 ### Major Features and Improvements > Arch * Some bugs fixed for spark computing engine > Component * Unified IO keys naming format for all components * Add LLMLoader to support running FATE-LLM v2.0 with pipeline > OSX * Compatible with eggroll-v2.x ## Release 2.0.0 ### Major Features and Improvements > Arch 2.0：Building Unified and Standardized API for Heterogeneous Computing Engines Interconnection * Introduce `Context` to manage useful APIs for developers, such as `Distributed Compting`, `Federation`, `Cipher`, `Tensor`, `Metrics`, and `IO`. * Introduce `Tensor` data structure to handle local and distributed matrix operation, with built-in heterogeneous acceleration support. * abstracted PHETensor, smooth switch between various underlying PHE implementations through standard interface * Introduce `DataFrame`, a 2D tabular data structure for data io and simple feature engineering * add data block manager to support mixed-type columns & feature anonymization * added 30+ operator interfaces for statistics, including comparison, indexing, data binning, and transformation, etc * Refactor `Federation`, a unified interface for federated computing. We provide a unified Serdes control and more user-friendly api. * Introduce `Config`, a unified configuration for FATE, including safety restrictions, system configuration, and algorithm configuration * Refactor `logger`, customizable logging for different use cases and flavors. * Introduce `Launcher`, a simple tool for federated program execution, especially useful for standalone and local debugging * Framework: PSI-ECDH protocol support, single entry for histogram statistical computation * Deepspeed integration: support distributed training using deepspeed with Eggroll. * Protocol: Support for SSHE(mpc and homomophic encryption mixed protocol), ECDH, Secure Aggregation protocols * Experimental Integrate `Crypten` for SMPC support, more protocols and features will be added in the future > 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 * Input-Output: Further decoupling of FATE-Flow, providing standardized black-box calling processes * Component Definition: Support for typing-based definition, automatic checking for component parameters, support for multiple types of data and model input and output, in addition to multiple inputs > ML 2.0: Major functionality migration from FATE-v1.x, decoupling call hierarchy * Data preprocessing: Added DataFrame Transformer; Reader, Union and DataSplit migration completed * Feature Engineering: Migrated HeteroFederatedBinning, HeteroFeatureSelection, DataStatistics, Sampling, FeatureScale and Pearson Correlation * Federated Training Migrated: HeteroSecureBoost, HomoNN, HeteroCoordinatedLogisticRegression, HeteroCoordinatedLinearRegression, SSHE-LogisticRegression and SSHE-LinearRegression * Federated Training Added: * SSHE-HeteroNN: based on mpc and homomorphic encryption mixed protocal * FedPASS-HeteroNN: based on fedpass protocol > Algorithm Performance Improvements: (Comparison with FATE-v1.11.*) * PSI (Privacy Set Intersection): tested on a dataset of 100 million with an intersection result of 100 million, 1.8+ times of FATE-v1.11.4 * Hetero-SSHE-LR: tested on data of guest 10w * 30 dimensions and host 10w * 300 dimensions, 4.3+ times of FATE-v1.11.4 * Hetero-NN(Based on FedPass Protocol): tested on data of guest 10w * 30 dimensions and host 10w * 300 dimensions, basically consistent with the plaintext performance, 143+ times of FATE-v1.11.4 * Hetero-Coordinated-LR: tested on data of guest 10w * 30 dimensions and host 10w * 300 dimensions, 1.2+ times of FATE-v1.11.4 * Hetero-Feature-Binning: tested on data of guest 10w * 30 dimensions and host 10w * 300 dimensions, 1.5+ times of FATE-v1.11.4 > OSX(Open Site Exchange) 1.0: Building Open Platform for Cross-Site Communication Interconnection * Implement the transmission interface in accordance with the “ Technical Specification for Financial Industry Privacy Computing Interconnection Platform”,The transmission interface is compatible with FATE 1.X version and FATE 2.X version * Supports GRPC synchronous and streaming transmission, supports TLS secure transmission protocol, and is compatible with FATE1.X rollsite components * Supports Http 1.X protocol transmission and TLS secure transmission protocol * Support message queue mode transmission, used to replace rabbitmq and pulsar components in FATE 1.X * Supports Eggroll and Spark computing engines * Supports networking as an Exchange component, with support for FATE 1.X and FATE 2.X access * Compared to the rollsite component, it improves the exception handling logic during transmission and provides more accurate log output for quickly locating exceptions. * The routing configuration is basically consistent with the original rollsite, reducing the difficulty of porting * Supports HTTP interface modification of routing tables and provides simple permission verification * Improved network connection management logic, reduced connection leakage risk, and improved transmission efficiency * Using different ports to handle access requests both inside and outside the cluster, facilitating the adoption of different security policies for different ports > FATE Flow 2.0: Building Open and Standardized Scheduling Platform for Scheduling Interconnection * Adapted to new scalable and standardized federated DSL IR * Built an interconnected scheduling layer framework, supported the BFIA protocol * Optimized process scheduling, with scheduling separated and customizable, and added priority scheduling * Optimized algorithm component scheduling，support container-level algorithm loading， enhancing support for cross-platform heterogeneous scenarios * Optimized multi-version algorithm component registration, supporting registration for mode of components * Federated DSL IR extension enhancement: supports multi-party asymmetric scheduling * Optimized client authentication logic, supporting permission management for multiple clients * Optimized RESTful interface, making parameter fields and types, return fields, and status codes clearer * Added OFX(Open Flow Exchange) module: encapsulated scheduling client to allow cross-platform scheduling * Supported the new communication engine OSX, while remaining compatible with all engines from FATE Flow 1.x * Decoupled the System Layer and the Algorithm Layer, with system configuration moved from the FATE repository to the Flow repository * Published FATE Flow package to PyPI and added service-level CLI for service management * Migrated major functionality from FATE Flow 1.x > Fate-Client 2.0: Building Scalable Federated DSL for Application Layer Interconnection And Providing Tools For Fast Federated Modeling. * Introduce new scalable and standardized federated DSL IR(Intermediate Representation) for federated modeling job * Compile python client to DSL IR * Federated DSL IR extension enhancement: supports multi-party asymmetric scheduling * Support mutual translation between Standardized Fate-2.0.0 DSL IR and BFIA protocol * Support using components with BFIA protocol through adapter mode * Migrated Flow CLI and Flow SDK > Fate-Test: FATE Automated Testing Tool * Migrated automated testing for functionality, performance, and correctness > Easy Deploy * Supports installation of FATE by PyPi