ipex-llm/docs/readthedocs/source/doc/PPML/Overview/trusted_fl.md

Trusted FL (Federated Learning)

SGX-based End-to-end Trusted FL platform

ID & Feature align

Before we start Federated Learning, we need to align ID & Feature, and figure out portions of local data that will participate in later training stage.

Let RID1 and RID2 be randomized ID from party 1 and party 2.

Vertical FL

Vertical FL training across multi-parties with different features.

Key features:

• FL Server in SGX
  • ID & feature align
  • Forward & backward aggregation
• Training node in SGX

Horizontal FL

Horizontal FL training across multi-parties.

Key features:

• FL Server in SGX
  • ID & feature align (optional)
  • Weight/Gradient Aggregation in SGX
• Training Worker in SGX

Example

Before running code

Prepare Docker Image

Build jar from Source

cd BigDL/scala/ppml && mvn clean package -DskipTests -Pspark_3.x
mv target/bigdl-ppml-spark_3.1.2-0.14.0-SNAPSHOT-jar-with-dependencies.jar demo
cd demo

Build Image

Modify your http_proxy in build-image.sh then run:

./build-image.sh

Prepare the Key

The ppml in bigdl needs secured keys to enable spark security such as Authentication, RPC Encryption, Local Storage Encryption and TLS, you need to prepare the secure keys and keystores. In this tutorial, you can generate keys and keystores with root permission (test only, need input security password for keys).

bash ../../../ppml/scripts/generate-keys.sh

You also need to generate your enclave key using the command below, and keep it safely for future remote attestations and to start SGX enclaves more securely.

It will generate a file enclave-key.pem in your present working directory, which will be your enclave key. To store the key elsewhere, modify the outputted file path.

openssl genrsa -3 -out enclave-key.pem 3072

Prepare the Password

Next, you need to store the password you used for key generation, i.e., generate-keys.sh, in a secured file.

bash ../../../ppml/scripts/generate-password.sh used_password_when_generate_keys

Then modify ENCLAVE_KEY_PATH to enclave-key.pem, DATA_PATH to BigDL/scala/ppml/demo/data(for example), KEYS_PATH to your-generated-keys and LOCAL_IP in deploy_fl_container.sh.

Start container

Running this command will start a docker container and initialize the sgx environment.

bash deploy_fl_container.sh
sudo docker exec -it flDemo bash
./init.sh

Start FLServer

In container, run:

./runFlServer.sh

The fl-server will start and listen on 8980 port. Both horizontal fl-demo and vertical fl-demo need two clients. You can change the listening port and client number by editing BigDL/scala/ppml/demo/ppml-conf.yaml's serverPort and clientNum.

HFL Logistic Regression

Open two new terminals, run:

sudo docker exec -it flDemo bash

to enter the container, then in a terminal run:

./runHflClient1.sh

in another terminal run:

./runHflClient2.sh

Then we start two horizontal fl-clients to cooperate in training a model.

VFL Logistic Regression

Open two new windows, run:

sudo docker exec -it flDemo bash

to enter the container, then in a terminal run:

./runVflClient1.sh

in another terminal run:

./runVflClient2.sh

Then we start two vertical fl-clients to cooperate in training a model.

References

1. Intel SGX
2. Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019. Federated Machine Learning: Concept and Applications. ACM Trans. Intell. Syst. Technol. 10, 2, Article 12 (February 2019), 19 pages. DOI:https://doi.org/10.1145/3298981