  Pervasive applications over large-scale, distributed embedded devices and the
Internet of Things (IoT) demand precise coordination with the network; for
example, several such applications, like collaborative video streaming and live
analysis, augmented reality, etc., need continuous monitoring of network
throughput and adapt the application behavior accordingly. Although the idea of
network throughput prediction is not new and quite dated, in this paper, we
show that the existing approaches fail to correctly infer the throughput when
the network operator or the device change, and thus, not generic enough for
Internet-scale applications. We propose \ourmethod, a novel approach that
allows collaborative training across different client hardware by capturing
throughput variations based on devices' sensitivity towards the corresponding
network configurations. Rigorous evaluations show that \ourmethod{} outperforms
various standard baseline algorithms with more than $80\%$ R2-score over
different datasets. We also analyze the performance of \ourmethod{} over a
network-aware streaming media application and demonstrate its efficacy for
various application scenarios.
