论文标题
多代理网络物理系统的弹性分布式优化
Resilient Distributed Optimization for Multi-Agent Cyberphysical Systems
论文作者
论文摘要
这项工作着重于多代理网络物理系统中的分布式优化问题，在该系统中，合法的代理商的迭代均受到从潜在恶意的邻近代理以及其自身自我服务的目标功能所获得的值的影响。我们开发了一种新的算法和分析框架，以实现存在并可以利用代理之间信任随机价值的一系列问题的弹性。在这种情况下，我们表明，即使是在存在恶意药物的情况下，也可以肯定地恢复与真正的全球最佳点的融合。此外，我们在预期的平方距离与最佳值的上限以上限的形式提供了预期的收敛速率保证。最后，即使恶意药物组成网络中的大多数代理，并且现有方法无法收敛到最佳标称点，验证我们的分析收敛保证也可以验证我们的分析收敛保证。
This work focuses on the problem of distributed optimization in multi-agent cyberphysical systems, where a legitimate agent's iterates are influenced both by the values it receives from potentially malicious neighboring agents, and by its own self-serving target function. We develop a new algorithmic and analytical framework to achieve resilience for the class of problems where stochastic values of trust between agents exist and can be exploited. In this case, we show that convergence to the true global optimal point can be recovered, both in mean and almost surely, even in the presence of malicious agents. Furthermore, we provide expected convergence rate guarantees in the form of upper bounds on the expected squared distance to the optimal value. Finally, numerical results are presented that validate our analytical convergence guarantees even when the malicious agents compose the majority of agents in the network and where existing methods fail to converge to the optimal nominal points.
