FedPAM

FedPAM is a federated framework for discrete Bayesian network learning using Probabilistic Adjacency Matrices (PAMs).

Datasets Used

1. Chronic Kidney Disease (CKD) prediction dataset:

   • Contains 400 samples with 24 discrete medical, laboratory, and demographic variables for binary classification of disease presence.
   • Stored in directory data/ckd_400 and is split into $K=3$ homogeneous client datasets.

2. Predict Students' Dropout and Academic Success:

   • Contains 4,424 samples with 36 variables for multi-class classification (3 classes) of academic outcomes based on enrollment information and first- and second-semester performance.
   • Stored in data/clients_datasets and is split into $K = 3$ to $10$ client datasets for both homogeneous and heterogeneous settings.

Config File

Modify the hyperparameters in config.yml file based on your requirements.

fc-fedpam:
  input:
    dataset_loc: "client.csv"
    target: 'class'
  split:
    mode: "file"
    dir: "."  
  mu: 0.3 
  lam: 0.3
  bootstrap_iterations: 100
  bootstrap_min_iterations: 5 
  bootstrap_patience: 5
  max_iterations: 100
  fl_min_iterations: 5 
  fl_patience: 5     
  homogeneous: True

Description of Hyperparameters:

1. dataset_loc: Location of the csv file containing the discrete dataset. During app testing , use the following directory structure:

data
└───clients_datasets_directory
│   └──client1
│       │client.csv
│   └──client2
│       │client.csv
│   └──client3
│       │client.csv

Check the data directory in the fc-fedpam repository before running the app to avoid any errors related to file paths. To test the app on the example datasets present in the data directory, change clients_datasets_directory to ckd_400, clients_datasets/clients_03 etc.

During actual federated workflow, you will be required to upload a shared config.yml file and a client.csv file containing the discrete dataset.

2. target: Set this to the prediction variable in the dataset. For CKD-400, use 'class' and for the students success prediction dataset, use 'Target'.

3. mode: Controls how the app finds data splits. If set to mode: 'directory', the app looks for subdirectories inside a base folder to use as separate client data splits. Otherwise, it uses the main /mnt/input directory as the single split. During testing, you can change client data directories using the FeatureCloud test-bed/workflow interface.

4. dir: The base directory (relative to /mnt/input) that contains subdirectories for each client's data split.

5. mu: Hyperparameter $\mu$ for tuning the proximal term $\frac{\mu}{2} \lVert P - P_{\text{global}} \rVert _F^2$ that encourages alignment with the global consensus PAM.

6. lam: Hyperparameter $\lambda$ for tuning the inner product between normalized Conditional Mutual Information (CMI) matrix $C$ and the PAM $P$ being optimized. The term $-\lambda \langle C, P\rangle$ promotes edges with high CMI.

7. bootstrap_iterations: Number of bootstrapping iterations $B$ for resampling over the dataset to create local PAM.

8. bootstrap_min_iterations: Minimum number of bootstrapping iterations $B_{min}$.

9. max_iterations: Total number of federated learning iterations.

10. fl_min_iterations: Minimum number of federated learning iterations.

11. fl_patience: Number of patience iterations for early stopping of the federated learning process if the average BIC score across clients does not improve for these many iterations.

12. homogeneous: Boolean hyperparameter to switch between homogeneous and heterogeneous learning modes. If the existing client data is "known" to be homogeneous, set homogeneous: true. Otherwise, set homogeneous: False. In fact, in real-world scenarios, keeping the latter is suggested as the client distributions are usually unknown.

Steps to run FedPAM application:

1. Install Docker and pip package featurecloud:

pip install featurecloud

2. Download the FedPAM image from FeatureCloud Docker repository using

featurecloud app download featurecloud.ai/fc-fedpam

3. OR build the app locally using:

featurecloud app build featurecloud.ai/fc-fedpam

Testing FedPAM Locally

To test FedPAM on locally stored datasets and simulate the federated learning workflow, you can use the FeatureCloud test-bed or FeatureCloud Workflow. You can also use CLI to run the app:

featurecloud test start --app-image featurecloud.ai/fc-fedpam --client-dirs './clients_datasets/clients_03/client1,./clients_datasets/
clients_03/client2,./clients_datasets/clients_03/client3' --generic-dir './generic'

Important: Keep the shared config.yml file in the generic directory.

The results of tests will be stored in fc-fedpam/data/tests.