FATE-Serving is a high-performance, industrialized serving system for federated learning models, designed for production environments.

FATE-Serving now supports

  • High performance online Federated Learning algorithms.
  • Federated Learning online inference pipeline.
  • Dynamic loading federated learning models.
  • Can serve multiple models, or multiple versions of the same model.
  • Support A/B testing experimental models.
  • Real-time inference using federated learning models.
  • Support multi-level cache for remote party federated inference result.
  • Support pre-processing, post-processing and data-access adapters for the production deployment.

Federated Learning Online Inference Pipeline



The code directory for FATE-Serving is FATE/fate-serving, which contains three parts:

  • fate-serving-core: base api for FATE-Serving.
  • federatedml: high performance online Federated Learning algorithms.
  • serving-server: Federated Learning online inference service based on GRPC.

After the compilation is complete, the fate-serving-core and federatedml will be included in the lib, and the serving-server is included in the jar package.

FATE-Serving has two profiles, one is the log4j2.xml for log settings, another is serving-server.properties.


Key configuration item description:

Configuration item Configuration item meaning Configuration item value
ip listen address for FATE-Serving default
port listen port for the grpc server of FATE-Serving default 8000
workMode the work mode of FATE-Flow 0 for standalone, 1 for cluster
inferenceWorkerThreadNum inference worker num for async inference default 10
standaloneStoragePath the storage path of standalone EggRoll generally is PYTHONPATH/data
remoteModelInferenceResultCacheSwitch switch of remote model inference result cache storage default true
proxy the address of proxy custom configuration
roll the address of roll custom configuration
OnlineDataAccessAdapter data access adapter class for obtaining host feature data default TestFile, read host feature data from host_data.csv on serving-server root directory
InferencePostProcessingAdapter inference post-processing adapter class for dealing result after model inference default pass
InferencePreProcessingAdapter inference pre-processing adapter class for dealing guest feature data before model inference default pass

Deploy Serving-Server

For detail, please refer to cluster deploy guide at cluster-deploy. Here are some key steps:

  • Compile in the arch directory
  • Compile in the fate-serving directory
  • Create your serving directory by referring to the cluster-deploy/example-dir-tree/serving-server directory
  • Copy fate-serving/serving-server/target/fate-serving-server-*.jar to serving-server directory
  • Copy fate-serving/serving-server/target/lib to serving-server directory

  • Copy fate-serving/serving-server/src/main/resources/* to serving-server/conf
  • Modify the configuration file according to the actual situation
  • Using the service.sh script to start/stop/restart


FATE-Serving provide publish model and online inference API.

Publish Model

Please use FATE-Flow Client which in the fate-flow to operate, refer to Online Inference guide at fate_flow_readme.


Serving currently supports three inference-related interfaces, using the grpc protocol.

  • inference: Initiate an inference request and get the result
  • startInferenceJob: Initiate an inference request task without getting results
  • getInferenceResult: Get the result of the inference by caseid

python examples/inference_request.py ${sering_host}

please refer to this script for inference.


Serving supports pre-processing, post-processing and data-access adapters for the actural production.

  • pre-processing: Data pre processing before model calculation
  • post-processing: Data post processing after model calculation
  • data-access: get feature from party’s system

At the current stage, you need to put the java code to recompile, and later support to dynamically load the jar in the form of a release.

For now:

  • push your pre-processing and post-processing adapter code into fate-serving/serving-server/src/main/java/com/webank/ai/fate/serving/adapter/processing and modify the InferencePreProcessingAdapter/InferencePostProcessingAdapter configuration parameters.
  • push your data-access adapter code into fate-serving/serving-server/src/main/java/com/webank/ai/fate/serving/adapter/dataaccess and modify the OnlineDataAccessAdapter configuration parameters.

please refer to PassPostProcessing, PassPreProcessing, TestFile adapter.

Remote party multi-level cache

For federal learning, one inference needs to be calculated by multiple parties. In the production environment, the parties are deployed in different IDCs, and the network communication between multiple parties is one of the bottleneck.

So, fate-serving supports caches multi-party model inference results on the initiator, but never caches feature data. you can turn the remoteModelInferenceResultCacheSwitch which in the configuration.


Federated Learning Online Inference Pipeline