As edge computing capabilities increase, model learning deployments in diverse edge environments have emerged. In experimental design networks, introduced recently, network routing and rate allocation are designed to aid the transfer of data from sensors to heterogeneous learners. We design efficient experimental design network algorithms that are (a) distributed and (b) use multicast transmissions. This setting poses significant challenges as classic decentralization approaches often operate on (strictly) concave objectives under differentiable constraints. In contrast, the problem we study here has a non-convex, continuous DR-submodular objective, while multicast transmissions naturally result in non-differentiable constraints. From a technical standpoint, we propose a distributed Frank-Wolfe and a distributed projected gradient ascent algorithm that, coupled with a relaxation of non-differentiable constraints, yield allocations within a $1-1/e$ factor from the optimal. Numerical evaluations show that our proposed algorithms outperform competitors with respect to model learning quality.

翻译：随着边缘计算能力的提升，在多样化边缘环境中部署模型学习已成为现实。在近期提出的实验设计网络中，网络路由与速率分配被设计用于协助数据从传感器传输至异构学习者。我们设计了高效的实验设计网络算法，这些算法具备：（a）分布式特性；（b）采用多播传输。这一场景带来了显著挑战——经典去中心化方法通常处理的是可微约束下的（严格）凹目标函数，而本研究所面对的目标函数具有非凸、连续DR-子模特性，多播传输更天然导致不可微约束条件。从技术层面，我们提出了分布式Frank-Wolfe算法与分布式投影梯度上升算法，通过松弛不可微约束，这些算法能够获得最优解$1-1/e$倍内的资源分配方案。数值评估结果表明，我们的算法在模型学习质量上优于现有竞争者。