  To prevent the leakage of private information while enabling 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 two tenets makes the algorithm
development quite challenging. In particular, how to efficiently search the
optimal neural architecture directly from massive non-iid data of clients in a
federated manner remains to be a hard nut to crack. To tackle this challenge,
in this paper, by leveraging the advances in proxy-less NAS, we propose a
Federated Direct Neural Architecture Search (FDNAS) framework that allows
hardware-aware NAS from decentralized non-iid data of clients. To further adapt
for various data distributions of clients, inspired by meta-learning, a cluster
Federated Direct Neural Architecture Search (CFDNAS) framework is proposed to
achieve client-aware NAS, in the sense that each client can learn a tailored
deep learning model for its particular data distribution. Extensive experiments
on real-world non-iid datasets show state-of-the-art accuracy-efficiency
trade-offs for various hardware and data distributions of clients. Our codes
will be released publicly upon paper acceptance.
