Federated Learning System with Homomorphic Encryption for Secure Training

The proliferation of data-driven artificial intelligence has created a strong incentive for organizations to pool their data for training more powerful and accurate machine learning models. However, this centralization of data creates significant privacy and security risks. Regulations such as GDPR and HIPAA strictly govern the use and transfer of personal information, making it legally and ethically challenging to move sensitive data from its source. Federated Learning (FL) was developed as a direct response to this challenge, enabling model training on decentralized data silos. In a typical FL setup, clients train a model locally and send only the resulting model updates (e.g., gradients) to a central server for aggregation. This process prevents the direct exposure of raw data. Despite this advancement, a critical vulnerability remains: the model updates themselves are not inherently private. Research has demonstrated that sophisticated inference and reconstruction attacks can be launched against these updates to deduce sensitive information about a client's private training data. An adversarial server, or any entity that intercepts the communication, could potentially reverse-engineer client data, thereby violating the core privacy promise of federated learning. This residual risk is a major barrier to adoption for risk-averse stakeholders in sectors like healthcare, where patient confidentiality is paramount, or finance, where transactional data is highly sensitive. These stakeholders require a 'zero-trust' guarantee where the central aggregator learns nothing about individual contributions. The central problem, therefore, is the lack of end-to-end confidentiality for model updates within the federated learning process. The aggregation server remains a trusted third party that must be assumed to be honest. This assumption is often untenable in real-world, cross-organizational collaborations. The challenge is to design a system that allows for the mathematically correct aggregation of model updates from multiple parties without revealing the content of those updates to the aggregating server or any other unauthorized party. Solving this problem requires moving beyond the standard FL paradigm to one that incorporates strong cryptographic guarantees directly into the aggregation mechanism, effectively making the aggregation process itself trustless and verifiably secure.

The proposed solution is a novel framework that synergizes Federated Learning (FL) with Homomorphic Encryption (HE) to create a highly secure, privacy-preserving machine learning environment. The architecture is designed to be trustless, meaning the central aggregation server can perform its function without ever having access to the unencrypted model updates from any client. This is achieved by implementing a cryptographic protocol that allows for computation directly on encrypted data. The core of the system will use an additively homomorphic encryption scheme, such as the Paillier cryptosystem, or a more advanced scheme like CKKS for approximate arithmetic, which is well-suited for machine learning applications involving floating-point numbers. The choice of scheme will be a key research component, balancing performance against the complexity of operations required. The workflow begins with a central server, which we will call the 'Coordinator', defining a machine learning task and distributing the initial global model to a cohort of participating clients. Each client trains this model on its local private data for one or more epochs to compute a model update (e.g., weight gradients). Critically, before transmitting this update back to the Coordinator, the client encrypts it using a public key. The Coordinator gathers these encrypted updates from all participating clients. Leveraging the homomorphic property of the encryption scheme, the Coordinator sums the encrypted updates together to produce a single aggregated ciphertext. This operation is performed without any knowledge of the underlying plaintext values, thus preserving the confidentiality of each client's contribution. The resulting encrypted aggregate update is then distributed back to the clients. Each client uses a corresponding private key to decrypt this aggregate, obtaining the cleartext global update. They then apply this update to their local model, completing a single round of federated training. The key management is a critical component of this architecture. A secure Key Management Service (KMS) will be designed to handle the generation, distribution, and rotation of the public/private key pairs. This project will implement the entire workflow as a containerized application using Docker, with clients and the server running as separate services. The backend will be developed in Python, utilizing the TensorFlow Federated framework for the FL orchestration and a library such as TenSEAL or Pyfhel for the homomorphic encryption primitives. This approach will result in a robust, deployable prototype that verifiably demonstrates secure model training on encrypted data.