Comments (4)
missing_gh_data[pid].encrypted = false;
missing_gh_data[pid].g_enc = 0, missing_gh_data[pid].h_enc = 0;
from fedtree.
Hi @blziz ,
In vertical FL, since one party (i.e., the aggregator) has the labels and can compute the raw gradients locally, it does not need to compute missing_gh based on encrypted gradients. The party with the labels will not send missing_gh to others so it's secure.
from fedtree.
Thank you! I confirmed that this situation occurs in parties without labels in vertical FL. The parameter settings are as follows
data=./dataset/test_dataset.txt
test_data=./dataset/test_dataset.txt
model_path=fedtree.model
partition_mode=vertical
n_parties=1
mode=vertical
privacy_tech=he
n_trees=40
depth=6
learning_rate=0.2
partition=1
and in homo_partition()
,
for (int i = 0; i < n_parties; i++) {
if (is_horizontal) { ... }
if (!is_horizontal) {
subsets[i].y = dataset.y;
if(i == 0)
subsets[i].has_label = false;
else
subsets[i].has_label = true;
}
...
}
from fedtree.
Hi @blziz ,
Thanks a lot for your information! There indeed exists possible security risks. The unencrypted missing_gh is caused by the sharing of the whole tree model among all parties in vertical FL, and the unencrypted missing_gh actually leaks no more information than the model itself. We are currently working on a version without sharing the whole model which is more secure. Also, we notice the following issues.
-
For homo_partition(), we find that the label splitting is not correct. In the simulation, when
i==0
(party id = 0), it is the host party and it should have the label. Otherwise, they are guest parties and are supposed to only have features. We have fixed it. -
You need to set n_parties >= 2 to simulate a reasonable federated learning scenario. In vertical FL, at least one of the parties has the labels. In our simulation, party 0 has the labels and the other parties do not have the labels.
from fedtree.
Related Issues (20)
- 安装问题 HOT 1
- python horizontal 模型训练自动killed问题 HOT 4
- How to train a random forests model? HOT 4
- Specify Data Partition in Horizontal Federated Tree Training HOT 1
- A Problem in Build on Linux HOT 2
- Install issue on MacOS HOT 5
- Is there any solution to bind `FedTree-train` program to specific cpu cores in standalone simulation? HOT 2
- Horizontal Federated Random Forest - Errors for Multiclass Classification HOT 1
- Error imputing more than 10 different data paths (files - .csv) HOT 2
- Is it an alternative way to install NTL? HOT 2
- documentation issue HOT 2
- why does it still require nvidia thrust cuda after set USE_CUDA to OFF HOT 1
- not working with grpc-1.53.0 & server still waiting after finish training HOT 8
- Error_when building with -DDISTRIBUTED=ON HOT 6
- A question about data partition in vertical FL in standalone simulation HOT 4
- Multi-class classification problem HOT 2
- Stack smashing when handling multiple csv files. HOT 3
- Feature Request: add support for "wait-for-ready"
- 可以在每一轮后输出测试集的评价指标吗? HOT 2
- prediction issue on vertical federated learning with he
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from fedtree.