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

Prepare environment

SGX

TO ADD

Get jar ready

Build from source

git clone https://github.com/intel-analytics/BigDL.git
cd BigDL/scala
./make-dist.sh

the jar would be BigDL/scala/ppml/target/bigdl-ppml...jar-with-dependencies.jar

Download pre-build

wget

Config

If deploying PPML on cluster, need to overwrite config ./ppml-conf.yaml. Default config (localhost:8980) would be used if no ppml-conf.yaml exists in the directory.

Start FL Server

java -cp com.intel.analytics.bigdl.ppml.FLServer

HFL Logistic Regression

We provide an example demo in BigDL/scala/ppml/demo

# client 1
java -cp com.intel.analytics.bigdl.ppml.example.HflLogisticRegression -d data/diabetes-hfl-1.csv

# client 2
java -cp com.intel.analytics.bigdl.ppml.example.HflLogisticRegression -d data/diabetes-hfl-2.csv

VFL Logistic Regression

# client 1
java -cp com.intel.analytics.bigdl.ppml.example.VflLogisticRegression -d data/diabetes-vfl-1.csv

# client 2
java -cp com.intel.analytics.bigdl.ppml.example.VflLogisticRegression -d data/diabetes-vfl-2.csv

References

Intel SGX
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

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