  Vertical Federated learning (VFL) is a class of FL where each client shares
the same set of samples but only owns a subset of the features. Usually, VFL
assumes perfect hardware and communication capabilities. However, this
assumption hinders the broad deployment of VFL, particularly on a network of
edge devices, which are heterogeneous in their in-situ capabilities while any
device may connect/disconnect from the network over time. To address this gap,
we study the test time performance of VFL under dynamic network conditions,
which we call DN-VFL. We first formalize DN-VFL, including a message passing
distributed inference algorithm, the corresponding risk, and a serverless
setup. We develop a novel DN-VFL approach called Multiple Aggregation with
Gossip Rounds and Simulated Faults (MAGS) that synthesizes replication,
gossiping, and selective feature omission to improve performance significantly
over baselines. Furthermore, we propose metrics and extensively analyze MAGS
using a simulated sensor network. The results show that naively using VFL for
DN-VFL is not the best approach. Rather, MAGS present a better alternative to
handle changes in the network during inference.
