Robust and Automated Reconfiguration of Byzantine Wide-Area Replication
2026-06-15 • Distributed, Parallel, and Cluster Computing
Distributed, Parallel, and Cluster ComputingCryptography and SecurityNetworking and Internet Architecture
AI summaryⓘ
The authors study how blockchain systems keep working correctly even if some parts act badly, by using copies of data and ordering transactions carefully. They noticed that changing who leads and how votes are counted based on network speed can make things faster, but also found that bad actors can trick this system. To fix this, the authors created Beware, a method that checks for fake network speed reports and uses machine learning to set up the system safely. Their tests show Beware can make the system almost half as fast again compared to older methods.
distributed systemsByzantine fault tolerancestate-machine replicationblockchain consensusleader electionvoting weightsnetwork latencymachine learningByzantine nodesconsensus latency
Authors
Rowdy Chotkan, Bulat Nasrulin, Johan Pouwelse, Jérémie Decouchant
Abstract
Distributed systems handle adversarial nodes through redundancy, which imposes a significant performance overhead. In blockchain systems, Byzantine fault-tolerant state-machine replication (BFT-SMR) is the replicated service that totally orders client transactions before execution. While prior research has primarily focused on designing novel consensus algorithms with improved performance, recent studies have shown that further gains can be achieved through configuration optimization. More precisely, replicas can monitor network latency to dynamically assign the leader role and tune voting weights, thereby improving consensus performance. However, we identify three vulnerabilities in this process that Byzantine nodes can exploit. To address these weaknesses, we propose Beware, a reconfiguration framework that filters out falsified latency reports, computes robust weight distributions, and applies machine learning to converge towards Byzantine-resilient configurations. Our evaluation shows that Beware reduces consensus latency by up to 45% compared to existing solutions.