Sustainable Federated Learning
This addresses sustainability challenges for smart ecosystems using wireless networks, though it appears incremental as it builds on existing federated learning with energy-aware adaptations.
The paper tackles the environmental impact of machine learning in wireless networks by introducing a sustainable federated learning framework that uses rechargeable devices with ambient energy harvesting, achieving significant performance improvements over energy-agnostic benchmarks.
Potential environmental impact of machine learning by large-scale wireless networks is a major challenge for the sustainability of future smart ecosystems. In this paper, we introduce sustainable machine learning in federated learning settings, using rechargeable devices that can collect energy from the ambient environment. We propose a practical federated learning framework that leverages intermittent energy arrivals for training, with provable convergence guarantees. Our framework can be applied to a wide range of machine learning settings in networked environments, including distributed and federated learning in wireless and edge networks. Our experiments demonstrate that the proposed framework can provide significant performance improvement over the benchmark energy-agnostic federated learning settings.