This dataset accompanies the manuscript "Telemetry-aware federated learning on heterogeneous edge devices: an experimental study". The dataset contains telemetry measurements collected from a federated learning testbed consisting of a Raspberry Pi 5 and an NVIDIA Jetson Nano running the Flower federated learning framework. Telemetry was sampled at one-second intervals and includes CPU utilization, CPU frequency, CPU temperature, memory usage, disk activity, network activity, system load, process counts, and Jetson-specific GPU metrics where available. The experiments comprise four stages: baseline system stress profiling, local model training on MNIST, Fashion-MNIST, and CIFAR-10 datasets, federated learning experiments under varying local-epoch settings, and controlled hardware throttling experiments to investigate device heterogeneity. The dataset supports reproducibility of the statistical analyses presented in the associated manuscript and may be useful for research on federated learning, edge computing, telemetry analytics, resource-aware scheduling, heterogeneous computing, and performance characterization of embedded AI platforms. The data are provided in Microsoft Excel format with accompanying metadata and are intended for academic and research use.
Manikandaprabu Nallasivam· Zenodo (CERN European Organi...· 0 citations
This dataset accompanies the manuscript "Telemetry-aware federated learning on heterogeneous edge devices: an experimental study". The dataset contains telemetry measurements collected from a federated learning testbed consisting of a Raspberry Pi 5 and an NVIDIA Jetson Nano running the Flower federated learning framework. Telemetry was sampled at one-second intervals and includes CPU utilization, CPU frequency, CPU temperature, memory usage, disk activity, network activity, system load, process counts, and Jetson-specific GPU metrics where available. The experiments comprise four stages: baseline system stress profiling, local model training on MNIST, Fashion-MNIST, and CIFAR-10 datasets, federated learning experiments under varying local-epoch settings, and controlled hardware throttling experiments to investigate device heterogeneity. The dataset supports reproducibility of the statistical analyses presented in the associated manuscript and may be useful for research on federated learning, edge computing, telemetry analytics, resource-aware scheduling, heterogeneous computing, and performance characterization of embedded AI platforms. The data are provided in Microsoft Excel format with accompanying metadata and are intended for academic and research use.
Manikandaprabu Nallasivam· Zenodo (CERN European Organi...· 0 citations
