Proof-Carrying Cryptography for Federated Learning

Abstract

Federated learning (FL) offers a promising approach to training machine learning models on decentralized datasets while preserving data privacy. However, current FL security mechanisms often rely on centralized trust assumptions, posing significant vulnerabilities. This paper proposes a novel security framework leveraging Proof-Carrying Cryptography (PCC) within the FL architecture. The core idea is to enable each participant to generate and maintain cryptographic proofs attesting to the correctness of their local model updates. These proofs are then presented to the central aggregator, who can verify the updates without needing direct access to the underlying data. This approach establishes a robust security layer based on verifiable computation, effectively mitigating the risks posed by malicious or compromised participants. We outline the technical details of integrating PCC into the FL process, addressing key challenges related to proof generation, verification, and scalability. The proposed framework significantly enhances the security and trustworthiness of FL systems, paving the way for broader adoption in sensitive applications.