A blockchain-orchestrated Federated Learning architecture for healthcare consortia

Abstract: We propose a novel architecture for federated learning within healthcare consortia. At the heart of the solution is a unique integration of privacy preserving technologies, built upon native enterprise blockchain components available in the Ethereum ecosystem. We show how the specific characteristics and challenges of healthcare consortia informed our design choices, notably the conception of a new Secure Aggregation protocol assembled with a protected hardware component and an encryption toolkit native to Ethereum. Our architecture also brings in a privacy preserving audit trail that logs events in the network without revealing identities.

A Blockchain-Orchestrated Federated Learning Architecture for Healthcare Consortia

This paper presents a federated learning architecture specifically tailored for healthcare consortia, characterized by the integration of privacy-preserving capabilities enabled through the Ethereum blockchain ecosystem. The proposed system addresses the complexities and requirements unique to healthcare environments by allowing for fine-grained data permissioning and ensuring secure aggregation and data transport protocols.

Key Elements of the Architecture

1. Data Permissioning and Secure Aggregation: The architecture allows data owners to exercise control over data access, specifying permissions for explicit consortium participants. Secure Aggregation is achieved through an augmented protocol utilizing AMD Secure Encrypted Virtualization (SEV), which replaces the previously vulnerable Multi-Party Computation (MPC) protocols, adapting better to the consortium context where fewer, more powerful nodes exist.
2. Peer-to-Peer Encryption: The updated model weights are encrypted during transmission between workers and the Secure Aggregator, employing Ethereum Improvement Proposal (EIP) 1024, which guarantees that sensitive information remains hidden from potentially dishonest network members.
3. Privacy Preserving Audit Trail: The architecture implements an immutable audit trail using Ethereum smart contracts, preventing unauthorized disclosures while maintaining transparency of operations for accountability purposes. This trail logs network events without exposing participant identities, aiding adherence to rigorous privacy mandates.

Implications and Potential Developments

The introduction of this blockchain-based federated learning architecture could revolutionize how sensitive health data is processed across institutional boundaries, emphasizing trustless processing and data sovereignty. By leveraging Ethereum's decentralized nature and smart contract functionalities, the solution adeptly balances data sharing utility with stringent privacy demands. Moreover, the use of tokens presents a mechanism to incentivize and reward data sharing and computation, potentially promoting wider adoption among participants in the health sector.

From a theoretical perspective, the approach redefines the protocols and security models applicable in federated learning settings by incorporating blockchain technology, which until now primarily found applications external to health data privacy and federated learning. The paper challenges established paradigms by proposing a reimagined interaction model between privacy-preserving computation and distributed ledger technology.

Looking ahead, future research could explore further enhancements in secure computation through alternative encryption standards or the inclusion of post-quantum cryptographic protocols to mitigate emerging security threats. As blockchain technology continues to evolve, its intersection with federated learning in domains necessitating high privacy standards such as healthcare, fosters the potential for robust, secure, and scalable solutions.

In conclusion, this paper contributes substantially to the discourse on privacy-preserving federated learning implementations within healthcare, offering a novel blueprint for the secure integration of blockchain technologies to meet the sector's distinct needs. As the field advances, both the practical efficacy and theoretical underpinnings of such architectures can be tested and refined, paving the way for more secure and efficient collaborations in sensitive data environments.