diff --git a/README.md b/README.md
index d95e433e8ee59c074884633f9469d2ffa2abbf22..895b45ec3a81e768bc42ce7e9fcf4b57c7dfee95 100644
--- a/README.md
+++ b/README.md
@@ -125,7 +125,7 @@ A full training or inference process in PFM consists of mainly three phases: dat
 
 A PFM program is exactly a PaddlePaddle program, and will be executed as normal PaddlePaddle programs. Before training/inference, user needs to choose a MPC protocol, define a machine learning model and their training strategies. Typical machine learning operators are provided in `paddle_fl.mpc` over encrypted data, of which the instances are created and run in order by Executor during run-time.
 
-For more information of Training/inference phase, please refer to the following [doc](./docs/source/md/mpc_train.md)
+For more information of Training/inference phase, please refer to the following [doc](./docs/source/md/mpc_train.md).
 
 #### C. Result reconstruction
 
@@ -149,6 +149,8 @@ We conduct tests on PFM using Boston house price dataset, and the implementation
 
 - Add K8S deployment scheme for Paddle Encrypted.
 
+- FL mobile simulator will be open sourced in following versions.
+
 ## Reference
 
 [1]. Jakub Konečný, H. Brendan McMahan, Daniel Ramage, Peter Richtárik. **Federated Optimization: Distributed Machine Learning for On-Device Intelligence.** 2016