  Neural networks often encounter various stringent resource constraints while
deploying on edge devices. To tackle these problems with less human efforts,
automated machine learning becomes popular in finding various neural
architectures that fit diverse Artificial Intelligence of Things (AIoT)
scenarios. Recently, to prevent the leakage of private information while enable
automated machine intelligence, there is an emerging trend to integrate
federated learning and neural architecture search (NAS). Although promising as
it may seem, the coupling of difficulties from both tenets makes the algorithm
development quite challenging. In particular, how to efficiently search the
optimal neural architecture directly from massive non-independent and
identically distributed (non-IID) data among AIoT devices in a federated manner
is a hard nut to crack. In this paper, to tackle this challenge, by leveraging
the advances in ProxylessNAS, we propose a Federated Direct Neural Architecture
Search (FDNAS) framework that allows for hardware-friendly NAS from non- IID
data across devices. To further adapt to both various data distributions and
different types of devices with heterogeneous embedded hardware platforms,
inspired by meta-learning, a Cluster Federated Direct Neural Architecture
Search (CFDNAS) framework is proposed to achieve device-aware NAS, in the sense
that each device can learn a tailored deep learning model for its particular
data distribution and hardware constraint. Extensive experiments on non-IID
datasets have shown the state-of-the-art accuracy-efficiency trade-offs
achieved by the proposed solution in the presence of both data and device
heterogeneity.
