存在恶意代理的分布式Optimization
On Distributed Optimization in the Presence of Malicious Agents
摘要：
本文考虑了一个由多个Agent组成的网络上的无约束分布式最优化问题，其中一些Agent是对抗性的。我们通过基于梯度的分布式优化算法来解决这个问题，并描述了对抗代理对算法收敛到最优解的影响。考虑的攻击模型是这样的代理本地扰动他们的迭代之前，广播到邻居，我们分析的情况下，敌对代理合作扰动他们的估计和每个敌对代理独立行事的情况。基于本文所采用的攻击模型，我们证明了解收敛到最优解的邻域，并依赖于攻击（扰动）项的大小。提出的分析建立条件下，恶意代理有足够的信息，以阻碍收敛到最优解的非对抗性代理。
英文摘要：
In this paper, we consider an unconstrained distributed optimization problem over a network of agents, in which some agents are adversarial. We solve the problem via gradient-based distributed optimization algorithm and characterize the effect of the adversarial agents on the convergence of the algorithm to the optimal solution. The attack model considered is such that agents locally perturb their iterates before broadcasting it to neighbors; and we analyze the case in which the adversarial agents cooperate in perturbing their estimates and the case where each adversarial agent acts independently. Based on the attack model adopted in the paper, we show that the solution converges to the neighborhood of the optimal solution and depends on the magnitude of the attack (perturbation) term. The analyses presented establishes conditions under which the malicious agents have enough information to obstruct convergence to the optimal solution by the non-adversarial agents.
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