@@ -163,12 +163,9 @@ A PFE program is exactly a PaddlePaddle program, and will be executed as normal
Upon completion of the secure training (or inference) job, the models (or prediction results) will be output by CPs in encrypted form. Result Parties can collect the encrypted results, decrypt them using the tools in PFE, and deliver the plaintext results to users.
## Install Guide and Quick-Start
Please reference [Quick Start](https://paddlefl.readthedocs.io/en/latest/instruction.html) for installation and quick-start example.
Please refer [K8S deployment example](./paddle_fl/examples/k8s_deployment/README.md) for details
You can also refer [K8S cluster application and kubectl installation](./paddle_fl/examples/k8s_deployment/deploy_instruction.md) to deploy your K8S cluster
### Paddle Encrypted
To be added.
## Benchmark task
### Horzontal Federated Learning
Gru4Rec [9] introduces recurrent neural network model in session-based recommendation. PaddlePaddle's Gru4Rec implementation is in https://github.com/PaddlePaddle/models/tree/develop/PaddleRec/gru4rec. An example is given in [Gru4Rec in Federated Learning](https://paddlefl.readthedocs.io/en/latest/examples/gru4rec_examples.html)
## Release note
- v0.2.0 released
- Support Kubernetes easy deployment
- Add api for [LEAF](https://arxiv.org/abs/1812.01097) dataset which is in federated settings, supporting benchmark experiments.
- Add FL-scheduler, acting as a central controller during the training phase.
- Add FL-Submitter to support cluster task submission
- Add secure aggregation algorithm
- Support more optimizers in PaddleFL such as Adam
