Advancing Federated LearnIng while Reducing tHe Carbon Footprint – DELIGHT

AI technologies today are too energy-intensive to be compatible with our sustainable development objectives. While recent work has assessed the carbon footprint of traditional learning methods, the carbon footprint of an emerging approach such as federated learning is not sufficiently studied. The DELIGHT project aims to evaluate and reduce the energy consumption of federated learning using different levers (gradient compression, data summarization, speed-scaling, etc.). Given the heterogeneity of the data, another objective will be to study the process of bargaining and coalition formation among nodes in order to understand to what extent a node has an interest in collaborating with others. The techniques developed will be empirically validated on computer vision and NLP tasks using the Flower toolkit.