  Federated Learning (FL) has gained increasing interest in recent years as a
distributed on-device learning paradigm. However, multiple challenges remain to
be addressed for deploying FL in real-world Internet-of-Things (IoT) networks
with hierarchies. Although existing works have proposed various approaches to
account data heterogeneity, system heterogeneity, unexpected stragglers and
scalibility, none of them provides a systematic solution to address all of the
challenges in a hierarchical and unreliable IoT network. In this paper, we
propose an asynchronous and hierarchical framework (Async-HFL) for performing
FL in a common three-tier IoT network architecture. In response to the largely
varied delays, Async-HFL employs asynchronous aggregations at both the gateway
and the cloud levels thus avoids long waiting time. To fully unleash the
potential of Async-HFL in converging speed under system heterogeneities and
stragglers, we design device selection at the gateway level and device-gateway
association at the cloud level. Device selection chooses edge devices to
trigger local training in real-time while device-gateway association determines
the network topology periodically after several cloud epochs, both satisfying
bandwidth limitation. We evaluate Async-HFL's convergence speedup using
large-scale simulations based on ns-3 and a network topology from NYCMesh. Our
results show that Async-HFL converges 1.08-1.31x faster in wall-clock time and
saves up to 21.6% total communication cost compared to state-of-the-art
asynchronous FL algorithms (with client selection). We further validate
Async-HFL on a physical deployment and observe robust convergence under
unexpected stragglers.
