End-to-End Verifiable Decentralized Federated Learning

TL;DR

This paper introduces an end-to-end verifiable decentralized federated learning system that ensures data integrity and authenticity from source to model aggregation, using blockchain and zero-knowledge proofs.

Contribution

It extends existing verifiable federated learning by incorporating end-to-end data integrity verification through a novel two-step proving and verification method.

Findings

Feasible implementation with marginal overheads

Enhanced data authenticity verification

Extension of blockchain and ZKP-based FL systems

Abstract

Verifiable decentralized federated learning (FL) systems combining blockchains and zero-knowledge proofs (ZKP) make the computational integrity of local learning and global aggregation verifiable across workers. However, they are not end-to-end: data can still be corrupted prior to the learning. In this paper, we propose a verifiable decentralized FL system for end-to-end integrity and authenticity of data and computation extending verifiability to the data source. Addressing an inherent conflict of confidentiality and transparency, we introduce a two-step proving and verification (2PV) method that we apply to central system procedures: a registration workflow that enables non-disclosing verification of device certificates and a learning workflow that extends existing blockchain and ZKP-based FL systems through non-disclosing data authenticity proofs. Our evaluation on a prototypical implementation demonstrates the technical feasibility with only marginal overheads to state-of-the-art solutions.