PoCQ: Proof of Contribution Quality as a Lightweight Blockchain Consensus for Secure Federated Learning

2026-06-03

AI Summary

This work addresses the vulnerability of decentralized federated learning to model poisoning attacks and the limitations of existing verification mechanisms, which suffer from unreliability and high computational overhead. The authors propose PoCQ, a lightweight blockchain consensus framework that uniquely integrates proof-of-contribution quality with reputation-driven consensus. PoCQ evaluates the quality of client updates through cryptographic commitments and norm-based validation, dynamically adjusts participant influence via a reputation mechanism, and stores only compact audit metadata on-chain to ensure scalability. Experimental results demonstrate that PoCQ significantly outperforms state-of-the-art approaches across three benchmark datasets, achieving a 34.1% accuracy gain under highly non-IID medical data settings, an 11% improvement in global average accuracy, and a 21.27% reduction in per-round verification time.

Abstract

Decentralized Federated Learning (FL) removes reliance on centralized coordinators but remains vulnerable to model poisoning, unreliable validation, and high validation overhead. This paper introduces Proof of Contribution Quality (PoCQ), a blockchain-based consensus framework designed to secure decentralized FL through reputation-aware validation and aggregation. PoCQ evaluates client updates using cryptographic commitments and lightweight norm-based validation, enabling efficient detection of malicious contributions while limiting validation cost. A reputation-driven consensus mechanism dynamically adjusts the influence of participants based on their historical contribution quality, while the blockchain stores only compact audit metadata to preserve scalability. Extensive experiments under poisoning scenarios across three benchmark datasets demonstrate that PoCQ outperforms the strongest state-of-the-art methods, achieving accuracy gains of 34.1% on challenging medical datasets in highly non-iid settings and an 11% improvement in global average accuracy. In addition, PoCQ reduces validation time by 21.27% on average per round, highlighting its effectiveness in jointly enhancing robustness and efficiency for fully decentralized federated learning.

Problem

• model poisoning
• unreliable validation
• high validation overhead
• decentralized federated learning
• blockchain consensus

Innovation

• Proof of Contribution Quality
• blockchain consensus
• federated learning security
• reputation-aware validation
• lightweight verification