Proving and Rewarding Client Diversity to Strengthen Resilience of Blockchain Networks

Client diversity is a cornerstone of blockchain resilience, yet most networks suffer from a dangerously skewed distribution of client implementations. This monoculture exposes the network to very risky scenarios, such as massive financial losses in the event of a majority client failure. In this paper, we present a novel framework that combines verifiable execution and economic incentives to provably identify and reward the use of minority clients, thereby promoting a healthier, more robust ecosystem. Our approach leverages state-of-the-art verifiable computation (zkVMs and TEEs) to generate cryptographic proofs of client execution, which are then verified on-chain. We design and implement an end-to-end prototype of verifiable client diversity in the context of Ethereum, by modifying the popular Lighthouse client and by deploying our novel diversity-aware reward protocol. Through comprehensive experiments, we quantify the practicality of our approach, from overheads of proof production and verification to the effectiveness of the incentive mechanism. This work demonstrates, for the first time, a practical and economically viable path to encourage and ensure provable client diversity in blockchain networks. Our findings inform the design of future protocols that seek to maximize the resilience of decentralized systems.

Authors

Institutions

Publication Details

Journal
Distributed Ledger Technologies Research and Practice
Published
2025-10-27
DOI
https://doi.org/10.1145/3773288
Citations
1
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
3.25
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Proving and Rewarding Client Diversity to Strengthen Resilience of Blockchain Networks

Zheyuan He, Martin Monperrus, Javier Ron
1 citations
Distributed Ledger Technologies Research and Practice
Blockchain Technology Applications and Security
3.25
article

Proving and Rewarding Client Diversity to Strengthen Resilience of Blockchain Networks

Zheyuan He, Martin Monperrus, Javier Ron
article en
1 citations

Abstract

Client diversity is a cornerstone of blockchain resilience, yet most networks suffer from a dangerously skewed distribution of client implementations. This monoculture exposes the network to very risky scenarios, such as massive financial losses in the event of a majority client failure. In this paper, we present a novel framework that combines verifiable execution and economic incentives to provably identify and reward the use of minority clients, thereby promoting a healthier, more robust ecosystem. Our approach leverages state-of-the-art verifiable computation (zkVMs and TEEs) to generate cryptographic proofs of client execution, which are then verified on-chain. We design and implement an end-to-end prototype of verifiable client diversity in the context of Ethereum, by modifying the popular Lighthouse client and by deploying our novel diversity-aware reward protocol. Through comprehensive experiments, we quantify the practicality of our approach, from overheads of proof production and verification to the effectiveness of the incentive mechanism. This work demonstrates, for the first time, a practical and economically viable path to encourage and ensure provable client diversity in blockchain networks. Our findings inform the design of future protocols that seek to maximize the resilience of decentralized systems.

Distributed Ledger Technologies Research and Practice
KTH Royal Institute of Technology (SE)
Openalex Percentile: Top 6%
Blockchain Technology Applications and Security
3.25
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.