ciferai/cifer repository overview

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Cifer enables privacy-preserving AI with Federated Learning (FL) and Fully Homomorphic Encryption (FHE). This Docker image is designed for infrastructure-level deployments and encrypted model training across distributed nodes.

• For a browser-based, no-code platform: (https://workspace.cifer.ai⁠)
• Documentation: (https://cifer.ai/docs⁠)
• PyPI: pip install cifer
• Slack: (https://ciferhub.slack.com⁠)

⁠Cifer Docker Image

The Cifer Docker image provides a secure, containerized environment for executing Privacy-Preserving Machine Learning (PPML) workflows. It enables scalable, distributed model training using Federated Learning (FL) and Fully Homomorphic Encryption (FHE)—ensuring that raw data remains protected at all times.

This image is ideal for engineers, infrastructure teams, and system integrators deploying Cifer in production or orchestration environments with container support.

⁠What is Cifer Federated Learning?

Cifer Federated Learning (FedLearn) is a secure training framework that enables collaborative machine learning across distributed data sources—without ever sharing raw data. Each participant (or node) performs local training, and only encrypted model updates are exchanged across the network.

Rather than centralizing data into a vulnerable repository, Cifer coordinates encrypted computations between participants, preserving data sovereignty, compliance, and confidentiality across jurisdictions and organizations.

⁠Key Extensions Beyond Standard FL

• Fully Homomorphic Encryption (FHE)
  Cifer integrates FHE at the protocol level, allowing model updates and gradients to be computed on encrypted tensors. This ensures data remains encrypted throughout the lifecycle—including training, aggregation, and communication.
  Unlike differential privacy (DP), which introduces noise and cannot fully prevent reconstruction attacks, FHE offers cryptographic guarantees against adversarial inference—even in hostile environments.
• Dual Topology Support: Centralized and Decentralized
  Cifer supports both:
  • Client–Server (cFL): A central coordinator aggregates updates from authenticated participants—ideal for trusted, enterprise-level deployments.
  • Peer-to-Peer (dFL): Participants can operate without a central aggregator, enabling direct encrypted update exchanges across nodes for higher resilience.

• Secure Communication Channels
  All communication is conducted over gRPC, leveraging HTTP/2 and Protocol Buffers for efficient, multiplexed, and encrypted transport. This ensures fast synchronization while minimizing attack surfaces.\
• Blockchain Integration (Optional)
  For use cases requiring immutable audit trails, decentralized identity, or consensus-based coordination, Cifer supports integration with its proprietary Cifer Blockchain Network, providing an additional layer of provenance and tamper resistance.

⁠Federated Learning and the Adversarial Threat Model

Standard federated learning protocols are susceptible to:

• Gradient leakage and model inversion attacks
• Malicious participant injection
• Data reconstruction through side-channel inference

The industry trend has been to use differential privacy (DP) to mitigate these threats. However:

• DP requires complex tuning of privacy budgets (ε, δ)
• It introduces statistical noise, reducing model accuracy
• It provides probabilistic—not cryptographic—guarantees, and can still leak information under repeated queries or cumulative exposure\

Cifer’s FHE-based design eliminates these risks by ensuring that all shared model artifacts remain mathematically unreadable, even under active attack or node compromise.

⁠Performance Capacity

Cifer FedLearn is built for real-world scale:

• Supports client-server and P2P topologies
• Tested for model sizes and parameter transfers up to 30GB
• Optimized for GPU acceleration, NUMA-aware compute, and multi-node orchestration

⁠Core Modules

• FedLearn
  Orchestrates decentralized training across multiple nodes while maintaining data locality. Supports both:
  • Centralized FL (cFL) for governed, trusted environments
  • Decentralized FL (dFL) with peer coordination across encrypted channels
• HomoCryption (FHE)
  Allows computation on encrypted data throughout the training lifecycle, preserving privacy even during intermediate operations.

⁠Key Capabilities

• Hybrid Federation Support
  Choose between cFL or dFL architectures depending on governance, trust, and fault tolerance requirements.
• Secure Communication Protocol
  Powered by gRPC with HTTP/2 and Protocol Buffers:
  • Low-latency streaming
  • Compact serialized messages
  • Built-in encryption and authentication
• End-to-End Encrypted Computation
  FHE is embedded directly into the training workflow. No intermediate decryption. Data privacy is mathematically guaranteed.\

⁠Platform Support

• Linux (Ubuntu 22.0)
• macOS 15.5
• Compatible with Docker, Docker Compose, Kubernetes, and other container orchestration systems

⁠Image Tags

⁠Quick Start

# Pull latest stable version
docker pull ciferai/cifer:latest

# Or pull a specific version
docker pull ciferai/cifer:1.0.21

Use the container as the base for:

• Secure multi-party ML pipelines
• Encrypted aggregation tasks
• Integration with PyTorch, TensorFlow, and custom FL workflows

⁠Python Package Integration

Cifer is also available on PyPI for programmatic workflows:

pip install cifer

Includes:

• Federated Learning APIs
• Encrypted tensor operations
• Secure aggregation, data preprocessing, and compliance tooling
• PyPI: https://pypi.org/project/cifer⁠
• Docs: https://cifer.ai/docs⁠
• Website: https://cifer.ai⁠
• Community: https://ciferhub.slack.com⁠