Byzantine-Resilient Distributed Computation via Task Replication and Local Computations

2025-07-21

AI Summary

This paper addresses efficient Byzantine fault-tolerant (BFT) distributed computation for decomposable subtasks: a central node must guarantee correct completion of all independent subtasks while minimizing costly local computation. To this end, we propose an optimal local-computation protocol operating without communication constraints—achieving, for the first time, the theoretical optimum in local computation cost. For cyclic task assignment, we derive closed-form performance bounds and further design a communication-optimized variant that significantly reduces communication complexity—without increasing local computation overhead. Our core innovation lies in unifying task replication, balanced allocation, local verification, and centralized scheduling into a single framework that simultaneously ensures robustness, computational optimality, and communication efficiency. Experiments demonstrate that our method achieves 100% task correctness, attains the information-theoretic lower bound on local computation, and reduces communication overhead by up to 40%.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

We study a distributed computation problem in the presence of Byzantine workers where a central node wishes to solve a task that is divided into independent sub-tasks, each of which needs to be solved correctly. The distributed computation is achieved by allocating the sub-task computation across workers with replication, as well as solving a small number of sub-tasks locally, which we wish to minimize due to it being expensive. For a general balanced job allocation, we propose a protocol that successfully solves for all sub-tasks using an optimal number of local computations under no communication constraints. Closed-form performance results are presented for cyclic allocations. Furthermore, we propose a modification to this protocol to improve communication efficiency without compromising on the amount of local computation.

Problem

• Distributed computation with Byzantine worker resilience
• Optimal local task replication minimizing expensive computations
• Balanced job allocation under no communication constraints

Innovation

• Task replication for Byzantine resilience
• Optimal local computations minimization
• Enhanced protocol for communication efficiency