docs: fix typos (#2087)

Co-authored-by: Yuan-Ting Hsieh (謝沅廷) <[email protected]>
NVIDIA · Oct 23, 2023 · 6e5540d · 6e5540d
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diff --git a/docs/glossary.rst b/docs/glossary.rst
@@ -116,7 +116,7 @@ server to connect to. This is only applicable in HA mode.
 
 Peer Context
 ============
-The :ref:`Peer Context <peer_context>` is the contextual information of the message sender that is sent in addtion to the
+The :ref:`Peer Context <peer_context>` is the contextual information of the message sender that is sent in addition to the
 regular payload of the message when the FL parties communicate with each other.
 
 Persistor

diff --git a/docs/programming_guide/controllers/cross_site_model_evaluation.rst b/docs/programming_guide/controllers/cross_site_model_evaluation.rst
@@ -26,4 +26,4 @@ example that implements the :class:`cross site model evaluation workflow<nvflare
 Example with Cross Site Model Evaluation / Federated Evaluation Workflow
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 See the `Hello Numpy Cross-Site Validation <https://github.com/NVIDIA/NVFlare/tree/main/examples/hello-world/hello-numpy-cross-val>`_ for an example application with
-the cross site model evaulation / federated evaluation workflow.
+the cross site model evaluation / federated evaluation workflow.
diff --git a/docs/real_world_fl/notes_on_large_models.rst b/docs/real_world_fl/notes_on_large_models.rst
@@ -5,7 +5,7 @@ Large Models
 As the federated learning tasks become more and more complex, their model sizes increase.  Some model sizes may go beyond 2GB and even reach hundreds of GB.  NVIDIA FLARE supports
 large models as long as the system memory of servers and clients is capable of handling it.  However, it requires special consideration on NVIDIA FLARE configuration and the system because
 the network bandwidth and thus the time to transmit such large amount of data during NVIDIA FLARE job runtime varies significantly.  Here we describe 128GB model training jobs to highlight
-the configuration and system that users should consider for sucessful large model federated learning jobs.
+the configuration and system that users should consider for successful large model federated learning jobs.
 
 System Deployment
 *****************

diff --git a/docs/user_guide/confidential_computing.rst b/docs/user_guide/confidential_computing.rst
@@ -8,7 +8,7 @@ Data used in NVFlare are encrypted during transmission between participants, whi
 data in transit is well protected.  Users can also utilize existing infrastructure, such as storage encryption, to protect data at rest.  With confidential computing, NVFlare can protect data in use
 and thus completes securing the entire lifecycle of data.
 
-Confidential computing in NVFlare is designed to explicitly establish the trust between participants.  Each participant must first capture the evidences related to the hardware (such as GPU), the software (GPU driver and VBIOS) and other componenets in its own platform.  The evidences will
+Confidential computing in NVFlare is designed to explicitly establish the trust between participants.  Each participant must first capture the evidences related to the hardware (such as GPU), the software (GPU driver and VBIOS) and other components in its own platform.  The evidences will
 be validated and signed to ensure its validity and authenticity.  The owner of signed evidence, called confidential computing token (CC token), can demonstrate the information about its computing environment to other
 participants by providing the CC token.  Upon receiving CC token, the participant (the relying party) can verify the claims inside the CC token against it own security policy on whether the CC token owner is
 using required hardware/software/components for security.  If the relying party finds the CC token does not meet its security policy, the relying party can inform the system that it chooses not to join the job deployment
@@ -99,4 +99,4 @@ After a submitted job is schedule to be deployed, the server verifies CC tokens
  all tokens from clients in the deployment map are verified successfully, those tokens will be sent to clients in deployment map for client side verification.  The client can determine whether it
  wants to join this job or not based on the result of verifying others' CC tokens against its own result policy.  If one client decides not to join the job, server will not deploy that job to that client.
 
-The server job scheduler will determine if the job has enough resources to be deployed and will determine the job's final status based on resource availablity and retry policy.
+The server job scheduler will determine if the job has enough resources to be deployed and will determine the job's final status based on resource availability and retry policy.
diff --git a/docs/user_guide/nvflare_cli/dashboard_command.rst b/docs/user_guide/nvflare_cli/dashboard_command.rst
@@ -28,7 +28,7 @@ Running ``nvflare dashboard -h`` shows all available options.
                             folder containing necessary info (default: current working directory)
     --passphrase PASSPHRASE
                             Passphrase to encrypt/decrypt root CA private key. !!! Do not share it with others. !!!
-    -e ENV, --env ENV     additonal environment variables: var1=value1
+    -e ENV, --env ENV     additional environment variables: var1=value1
     --cred CRED           set credential directly in the form of USER_EMAIL:PASSWORD
     -i IMAGE, --image IMAGE
                             set the container image name

diff --git a/docs/user_guide/nvflare_cli/poc_command.rst b/docs/user_guide/nvflare_cli/poc_command.rst
@@ -1,7 +1,7 @@
 .. _poc_command:
 
 *****************************************
-Commmand for Proof Of Concept (POC) Mode
+Command for Proof Of Concept (POC) Mode
 *****************************************
 
 Introduction to the POC Command