Year: 2024
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Institutions: Northeastern University, Carnegie Mellon University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
Awards: AI Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE), Collaborative Research: CNS Core: Medium: Data-Centric Networks for Distributed Learning, Collaborative Research: CNS Core: Medium: Data-Centric Networks for Distributed Learning, NeTS: Small: Caching Networks with Optimality Guarantees
OpenAlex ID: W4390810353