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Byzantine-Resilient Multiagent Optimization
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DOI：10.1109/TAC.2020.3008139.png)
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Abstract
En 中文
We consider the problem of multiagent optimization wherein an unknown subset of agents suffer Byzantine faults and thus behave adversarially. We assume that each agent i has a local cost function f(i), and the overarching goal of the good agents is to collaboratively minimize a global objective that properly aggregates these local cost functions. To the best of our knowledge, we are among the first to study Byzantine-resilient optimization where no central coordinating agent exists, and we are the first to characterize the structures of the convex coefficients of the achievable global objectives. Dealing with Byzantine faults is very challenging. For example, in contrast to fault-free networks, reaching Byzantine-resilient agreement even in the simplest setting is far from trivial. We take a step toward solving the proposed Byzantine-resilient multiagent optimization problem by focusing on scalar local cost functions. Our results might provide useful insights for the general local cost functions.
Keywords:
Cost function
Consensus algorithm
Aggregates
Computational modeling
Focusing
Standards
Computational and artificial intelligence
fault tolerance
fault tolerant control
machine learning
distributed computing
reliability
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