GreenFedEnv: Code and experimental results for energy-aware federated learning under communication and privacy constraints for IoT sensing
Abstract
Reproducibility package for the article "Energy-Aware Federated Learning under Communication and Privacy Constraints for IoT Sensing." It contains the complete source code of the GreenFedEnv federated-learning framework (top-k sparsification with error feedback, stochastic quantization, straggler-tolerant partial participation, client-level differential privacy, and a grid-intensity-independent carbon-accounting model), the run configuration, the pre-registered experiment plan, and every numerical result behind the tables and figures in the article: per-seed forecasting and anomaly-detection measurements, communication/energy/carbon accounting, and sweeps over compression density, client participation, the privacy budget, and federation size, each over five seeds with Holm-corrected significance tests against FedAvg. Training curves for the convergence plots are included as JSON. The two input benchmark datasets (Beijing Multi-Site Air-Quality and UCI ElectricityLoadDiagrams20112014) are not redistributed here; they are obtained from the UCI Machine Learning Repository as described in data/README.md, and the simulated sensor network is generated deterministically by the code. Python 3.11, CPU-only; see requirements.txt and REPRODUCE.md for exact commands.
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