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

Revisiting Cellular Throughput Prediction over the Edge: Collaborative Multi-device, Multi-network in-situ Learning