Decentralized Accountability in Federated Learning: Design and Critique of a Smart Contract-Based Reputation Mechanism

Abstract

The use of Federated Learning (FL) in sensitive, multi-party settings has made it even more important to have trust, accountability and safe systems of governance have become increasingly critical in the context of Federated Learning (FL) being utilized in sensitive multi-party environments. FL defends locality of data, but still can be poisoned with models, tampered with malicious gradient modification and unstable client behaviour due to lack of trust verification. The current paper examines how integrating Bloackchain Technology into FL(BCFL) provides an unquestioned system of governance that facilitates clear client accountability, model provenance tracing and record tampering resistant record management. We integrate the architectural and cryptographic and consensus conditions that are necessary in the development of robust BCFL systems with a special focus on the lightweight and reputation based processes of consensus. As a case in point, we critically examine an example of solidity-based prototype, ReputationManager.sol, which executes a Proof-of-Reputation (PoR) mechanism in which the aggregation weight of each client in a model is determined by its past integrity. According to our review, we find there is a Centralization Paradox in that, despite the implementation being based on a decentralized ledger, the prototype is premised on singlet owner access control, delegating trustlessness to a centralized blockchain administrator, and creating a single point of failure. We achieve this by pointing out important future directions including decentralized PoR models, automatic reputation updates in the basis of cryptographically checkable conduct and the introduction of Zero-Knowledge Proofs to formulate privacy preserving, regulation conformable and truly trustworthy BCFL regulation.