Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems. (arXiv:2305.07135v1 [cs.LG])

Federated Learning (FL) is a privacy-preserving distributed machine learning
approach geared towards applications in edge devices. However, the problem of
designing custom neural architectures in federated environments is not tackled
from the perspective of overall system efficiency. In this paper, we propose
DC-NAS -- a divide-and-conquer approach that performs supernet-based Neural
Architecture Search (NAS) in a federated system by systematically sampling the
search space. We propose a novel diversified sampling strategy that balances
exploration and exploitation of the search space …

applications architectures arxiv devices distributed edge edge devices efficiency environments federated learning machine machine learning nas neural architectures paper perspective privacy puzzle systems