CryptFlow: Federated Learning with Homomorphic Encrypted Model Aggregation

The central challenge this project addresses is the inherent tension between the need for large-scale data in machine learning and the non-negotiable requirement for robust data privacy. State-of-the-art models in fields such as medical diagnostics, financial fraud detection, and natural language processing are data-hungry, yet the sensitive nature of this data erects formidable barriers to its collection and use. Centralized data lakes, the traditional solution, represent a massive liability, creating a single, high-value target for cyberattacks and posing significant regulatory compliance risks. This centralization model is increasingly unsustainable in a world governed by strict privacy laws that mandate data minimization and user control. Federated Learning (FL) was conceived as a direct response to this problem, decentralizing model training to the data's source. By only transmitting model parameter updates (e.g., gradients or weights) instead of the data itself, FL drastically reduces privacy risks. However, this approach introduces a new, more subtle set of vulnerabilities. Academic research has demonstrated that these model updates are not innocuous; they can be exploited by a curious or malicious aggregation server to infer properties of the client's private training data. Techniques like membership inference attacks can determine if a specific data point was used in training, while more advanced reconstruction attacks can even recreate representative samples of the training data. This residual privacy leakage means that participants must still place a significant amount of trust in the central aggregator, which remains a privileged entity with access to all participant contributions. This trust requirement is the critical flaw in standard FL. In many real-world collaborative scenarios, such as competing hospitals training a shared cancer detection model, no single institution can or should be trusted with the model updates from others. The core problem, therefore, is the absence of a practical and performant mechanism for 'zero-knowledge' aggregation, where a global model can be computed from individual contributions without revealing those contributions to any party, including the central server. Without solving this secure aggregation problem, the full potential of Federated Learning in high-stakes, multi-stakeholder environments cannot be realized, leaving valuable data siloed and impeding scientific and commercial progress.

The proposed solution is CryptFlow, a comprehensive framework that integrates homomorphic encryption into the core of the federated learning process to enable truly private model aggregation. CryptFlow is designed as a modular, scalable system composed of three primary components: the Client Agent, the Secure Aggregator, and the Key Management Service (KMS). This architecture explicitly decouples the machine learning task from the cryptographic security, allowing for flexibility in both domains. The workflow is designed to eliminate the trust requirement placed on the central server, ensuring that individual model updates remain confidential throughout the entire lifecycle of a training round. In a typical CryptFlow training round, the process begins with the Secure Aggregator defining a new training task and the KMS generating a fresh public/private key pair for the chosen HE scheme (e.g., CKKS for approximate number arithmetic suitable for deep learning). The public key is distributed to all participating clients. Each client then proceeds with the standard FL step of training its local model on its private data to compute a model update (delta). Crucially, before transmitting this update, the Client Agent uses the public key to encrypt the entire weight or gradient tensor. These encrypted tensors, or ciphertexts, are then sent to the Secure Aggregator. This is the core of our security proposition: the Aggregator receives a collection of opaque ciphertexts and has no mathematical way to discern the underlying values. The Secure Aggregator's role is transformed from a trusted curator to a non-trusted computational oracle. It leverages the homomorphic properties of the encryption—specifically, homomorphic addition—to sum all the received ciphertexts. This operation results in a single ciphertext that encrypts the sum of all individual model updates. The Aggregator then performs any necessary scaling (e.g., averaging) homomorphically. The resulting encrypted global model update is sent back to the participating clients. Each client, using the private key they securely hold (or receive from the KMS), decrypts this global update and applies it to their local model. The private key is never shared with the Aggregator. This entire process ensures end-to-end confidentiality of model contributions, mitigating risks of inference attacks by the central server. To address the significant computational overhead associated with HE, CryptFlow will incorporate several optimizations. We will utilize the CKKS scheme, which is optimized for real-number arithmetic and allows for efficient vectorization of operations through SIMD (Single Instruction, Multiple Data) packing. The implementation will leverage highly optimized C++ libraries like Microsoft SEAL or PALISADE, with Python bindings for seamless integration with ML frameworks like TensorFlow and PyTorch. The system architecture will be designed for horizontal scalability, using a microservices-based approach with Docker and Kubernetes to manage the Aggregator and KMS components. The project will involve rigorous benchmarking to quantify the overhead and identify bottlenecks, with research focused on optimizing HE parameters and communication protocols to find the 'sweet spot' between security, accuracy, and performance for practical, real-world deployments.