Fault-Tolerant Vertical Federated Learning on Dynamic Networks
Surojit Ganguli Zeyu Zhou Christopher G. Brinton David I. Inouye
Vertical Federated learning (VFL) is a class of FL where each client shares
the same sample space but only holds a subset of the features. While VFL
tackles key privacy challenges of distributed learning, it often assumes
perfect hardware and communication capabilities. This assumption hinders the
broad deployment of VFL, particularly on edge devices, which are heterogeneous
in their in-situ capabilities and will connect/disconnect from the network over
time. To address this gap, we define Internet Learning (IL) including its data
splitting and network context and which puts good performance under extreme
dynamic condition of clients as the primary goal. We propose VFL as a naive
baseline and develop several extensions to handle the IL paradigm of learning.
Furthermore, we implement new methods, propose metrics, and extensively analyze
results based on simulating a sensor network. The results show that the
developed methods are more robust to changes in the network than VFL baseline.