Applying homomorphic encryption for security and privacy

How it works with Federated Learning

Homomorphic encryption is an optional encryption method to add additional security and privacy to a Federated Learning experiment. When homomorphic encryption is applied in a Federated Learning experiment, the parties send their homomorphically encrypted model updates to the aggregator. The aggregator does not have the private key and can only see the homomorphically encrypted model updates. For example, the aggregator cannot reverse engineer the model updates to discover information on the parties' training data. The aggregator fuses the model updates in their encrypted form which results in an encrypted aggregated model. Then the aggregator sends the encrypted aggregated model to the participating parties who can use their private key for decryption and continue with the next round of training. Only the participating parties can decrypt model data.

Supported frameworks and fusion methods

Fully Homomorphic Encryption (FHE) supports the simple average fusion method for these model frameworks:

• Tensorflow
• Pytorch
• Scikit-learn classification
• Scikit-learn regression

Before you begin

To get started with using homomorphic encryption, ensure that your experiment meets the following requirements:

• The hardware spec must be minimum small. Depending on the level of encryption that you apply, you might need a larger hardware spec to accommodate the resource consumption caused by more powerful data encryption. See the encryption level table in Configuring the aggregator.- The software spec is fl-rt22.2-py3.10.

• FHE is supported in Python client version 1.0.263 or later. All parties must use the same Python client version.

Configuring the aggregator (admin)

As you create a Federated Learning experiment, follow these steps:

1. In the Configure tab, toggle "Enable homomorphic encryption".
2. Choose small or above for Hardware specification. Depending on the level of encryption that you apply, you might need a larger hardware spec to accommodate the resource consumption for homomorphic encryption.
3. Ensure that you upload an unencrypted initial model when selecting the model file for Model specification.
4. Select "Simple average (encrypted)" for Fusion method. Click Next.
5. Check Show advanced in the Define hyperparameters tab.
6. Select the level of encryption in Encryption level.
   Higher encryption levels increase security and precision, and require higher resource consumption (e.g. computation, memory, network bandwidth). The default is encryption level 1.
   See the following table for description of the encryption levels:

Security is the strength of the encryption, typically measured by the number of operations that an attacker must perform to break the encryption.
Precision is the precision of the encryption system's outcomes. Higher precision levels reduce loss of accuracy of the model due to the encryption.

Connecting to the aggregator (party)

The following steps only show the configuration needed for homomorphic encryption. For a step-by-step tutorial of using homomorphic encryption in Federated Learning, see FHE sample.

To see how to create a general end-to-end party connector script, see Connect to the aggregator (party).

1. Install the Python client with FHE with the following command:
   pip install 'ibm_watson_machine_learning[fl-rt23.1-py3.10,fl-crypto]'

2. Configure the party as follows:

   party_config = {
       "local_training": {
           "info": {
               "crypto": {
                   "key_manager": {
                       "key_mgr_info": {
                           "distribution": {
                               "ca_cert_file_path": "path of the root certificate file identifying the certificate authority",
                               "my_cert_file_path": "path of the certificate file of the party issued by the certificate authority",
                               "asym_key_file_path": "path of the RSA key file of the party"
                               } 
                           }  
                       }  
                   }  
               }  
           }  
       }
   }
   

3. Run the party connector script after configuration.