Cifer is a Privacy-Preserving Machine Learning (PPML) framework offers several methods for secure, private, collaborative machine learning “Federated Learning” and “Fully Homomorphic Encryption”
Cifer is a Privacy-Preserving Machine Learning (PPML) framework designed to revolutionize the way organizations approach secure, private, and collaborative machine learning. In an era where data privacy and security are paramount, Cifer offers a comprehensive solution that combines advanced technologies to enable privacy-conscious AI development and deployment.
Flexible Architecture: Cifer adapts to your needs, supporting both decentralized and centralized federated learning configurations.
Enhanced Security and Privacy: Leveraging advanced cryptographic techniques and secure communication protocols, Cifer provides robust protection against various privacy and security threats.
Broad Integration: Seamlessly integrates with popular machine learning tools and frameworks, including PyTorch, TensorFlow, scikit-learn, NumPy, JAX, Cuda, Hugging Face's Transformer; ensuring easy adoption across different environments.
No-Code Configuration: Simplify your setup with our intuitive no-code platform, making privacy-preserving machine learning accessible to a wider audience.
Cifer offers a revolutionary approach to Privacy-Preserving Machine Learning (PPML) by combining powerful federated learning capabilities with robust encryption, ensuring privacy, security, and flexibility. Here are the key reasons why Cifer sets itself apart from other federated learning frameworks:
Cifer’s FedLearn framework provides the flexibility to choose between Decentralized Federated Learning (dFL) and Centralized Federated Learning (cFL):
Decentralized Federated Learning (dFL): Powered by Cifer’s proprietary blockchain and Layer-1 infrastructure, dFL ensures a robust, resilient system through its Byzantine Robust Consensus algorithm, even if some nodes are compromised or malicious. This fully decentralized approach is ideal for distributed environments where privacy and data ownership are paramount.
Centralized Federated Learning (cFL): For organizations that prefer more control, such as trusted collaborations among known partners, cFL offers a centralized model that provides oversight and management flexibility. This centralized option is tailored for environments that require higher levels of governance.
Most federated learning frameworks on the market rely on Peer-to-Peer (P2P) protocols, which are vulnerable to security threats like man-in-the-middle attacks, data interception, and inefficiencies in communication.
Cifer uses the gRPC communication protocol, which leverages HTTP/2 for multiplexing, bidirectional streaming, and header compression, resulting in faster, more secure communication. By utilizing Protocol Buffers for serialization, Cifer ensures smaller message sizes, faster processing, and enhanced reliability. The built-in encryption and secure communication channels protect data exchanges from unauthorized access and tampering, making Cifer a more secure and efficient solution compared to P2P-based frameworks.
Cifer simplifies the complexity of setting up federated learning with its no-code configuration platform. Unlike other frameworks that require manual coding and intricate setups, Cifer provides an intuitive browser-based user interface that allows users to design, configure, and deploy federated learning systems without writing any code. This innovative approach lowers the barrier for organizations to adopt federated learning while ensuring flexibility and scalability.
Cifer uniquely combines FedLearn with Fully Homomorphic Encryption (FHE), enabling computations on encrypted data throughout the entire training process. This means that sensitive data never needs to be decrypted, providing end-to-end encryption for complete privacy. With the integration of FHE, organizations can train machine learning models on sensitive data without ever exposing it, ensuring that privacy and compliance standards are met, even when working in a collaborative environment.
Pip The preferred way to install Cifer is through PyPI:
pip install cifer
To upgrade Cifer to the latest version, use:
pip install --upgrade cifer
Note:
- For macOS: You can run these commands in the Terminal application.
- For Windows: Use Command Prompt or PowerShell.
- For Linux: Use your preferred terminal emulator.
- For Google Colab: Run these commands in a code cell, prefixed with an exclamation mark (e.g., !pip install cifer).
- For Jupyter Notebook: You can use either a code cell with an exclamation mark or the %pip magic command (e.g., %pip install cifer).
You can get the Cifer docker image by pulling the latest version:
docker pull ciferai/cifer:latest
To use a specific version of Cifer, replace latest with the desired version number, for example:
docker pull ciferai/cifer:v1.0.0
When you install Cifer using pip, you get access to the following components and features:
Cifer seamlessly integrates with popular machine learning frameworks: TensorFlow, Pytorch, scikit-learn, Numpy, Cuda, JAX, Hugging Face’s Transformer
Some features may require additional dependencies. You can install them using:
pip install cifer[extra]
Where extra can be:
viz: For visualization tools
gpu: For GPU acceleration support
all: To install all optional dependencies
After installing Cifer, you can import its modules in your Python scripts or interactive environments. The two main modules, FedLearn and FHE (Fully Homomorphic Encryption), can be imported as follows:
Python
from cifer import fedlearn as fl
from cifer import homocryption as fhe
Here are some quick examples to get you started:
# Import the necessary modules from the Cifer framework
from cifer import fedlearn as fl
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
import os
# Define paths to local data and model
local_data_path = "/path/to/local/data"
local_model_path = "/path/to/local/model"
# Option to load a pre-trained Hugging Face model
model_name = "distilbert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Alternatively, clone a model repository from GitHub (if necessary)
os.system("git clone https://github.com/your-repo/your-model-repo.git")
# Initialize the federated learning server using Cifer's FedLearn
server = fl.Server()
# Define a federated learning strategy
strategy = fl.strategy.FedAvg(
# Custom data and model paths for local storage and Hugging Face model usage
data_path=local_data_path,
model_path=local_model_path,
model_fn=lambda: model, # Hugging Face model used as the base model
)
# Start the federated learning process
if __name__ == "__main__":
server.run(strategy)
The code begins by importing Cifer’s federated learning module: from cifer import fedlearn as fl, which allows you to use the FedLearn framework in your federated learning setup.
The dataset is stored locally, and the path to the dataset is defined using local_data_path. Ensure your dataset is prepared and accessible in the specified directory on your local machine. This local path will be used to load data for federated learning:
local_data_path = "/path/to/local/data"
You can integrate models into Cifer’s FedLearn in three different ways, depending on your requirements:
3.1 Local Model:
If you have a pre-trained model stored locally, you can specify the local path to the model and use it for training:
local_model_path = "/path/to/local/model"
3.2 Git Clone:
If your model is hosted on GitHub, you can clone the repository directly into your environment using the os.system("git clone ...") command:
os.system("git clone https://github.com/your-repo/your-model-repo.git")
3.3 Hugging Face Model:
You can integrate a pre-trained model from Hugging Face’s transformers library. For instance, you can load a BERT-based model like this:
model_name = "distilbert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Import Cifer's HomoCryption module for fully homomorphic encryption
from cifer import homocryption as hc
# Generate keys: Public Key, Private Key, and Relinearization Key (Relin Key)
public_key, private_key, relin_key = hc.generate_keys()
# Example data to be encrypted
data = [42, 123, 256]
# Encrypt the data using the Public Key
encrypted_data = [hc.encrypt(public_key, value) for value in data]
# Perform computations on encrypted data
# For example, adding encrypted values
encrypted_result = hc.add(encrypted_data[0], encrypted_data[1])
# Apply relinearization to reduce noise in the ciphertext
relinearized_result = hc.relinearize(encrypted_result, relin_key)
# Decrypt the result using the Private Key
decrypted_result = hc.decrypt(private_key, relinearized_result)
# Output the result
print("Decrypted result of encrypted addition:", decrypted_result)
First, we generate the necessary keys for homomorphic encryption using hc.generate_keys(). This provides the Public Key (used for encrypting data), Private Key (for decrypting results), and Relinearization Key (used to reduce noise during operations on encrypted data).
Data is encrypted using the Public Key with hc.encrypt(). In this example, a simple array of numbers is encrypted for further computations.
Fully homomorphic encryption allows computations to be performed directly on encrypted data. Here, we add two encrypted values with hc.add() without decrypting them, maintaining privacy throughout the operation.
Relinearization helps manage noise introduced by homomorphic operations, which is done with the Relin Key using hc.relinearize().
After the computations are complete, the Private Key is used to decrypt the result with hc.decrypt().
For more detailed information and access to the full documentation, please visit www.cifer.ai
FedServer class that supports federated learning using gRPC.clientRegister.startServer function.__callModelValidation method.config.json.max_receive_message_length via the config.json file.-- Integrate Tensorflow and Huggingface's Transformer New Integration: Added support for TensorFlow and HuggingFace's Transformers library to enhance model training and expand compatibility with popular AI frameworks.
-- Resolved various bugs to improve system stability and performance. This update continues to build on CiferAI's federated learning and fully homomorphic encryption (FHE) framework, focusing on enhanced compatibility, privacy, and security in decentralized machine learning environments.
README.md to improve content and information about Cifer.FAQs
Federated Learning and Fully Homomorphic Encryption
We found that cifer demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 4 open source maintainers collaborating on the project.
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