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.