GreenFLag: A Green Agentic Approach for Energy-Efficient Federated Learning

TL;DR

GreenFLag is a reinforcement learning-based framework that significantly reduces grid energy consumption in federated learning by optimizing resource use and integrating renewable energy sources.

Contribution

It introduces GreenFLag, a novel agentic resource orchestration framework that minimizes grid energy use in federated learning while maintaining model performance.

Findings

GreenFLag reduces grid energy consumption by 94.8% on average.

It effectively integrates renewable energy sources into federated learning workflows.

GreenFLag outperforms three state-of-the-art baselines in energy efficiency.

Abstract

Progressing toward a new generation of mobile networks, a clear focus on integrating distributed intelligence across the system is observed to drive performance, autonomy, and real-time adaptability. Federated learning (FL) stands out as a key emerging technique, enabling on-device model training while preserving data locality. However, its operation introduces substantial energy and resource demands. Energy needs are mostly met by grid power sources, while FL resource orchestration strategies remain limited. This work introduces GreenFLag, an agentic resource orchestration framework designed to minimize the energy consumption from the grid power to complete FL workflows, guarantee FL model performance, and reduce grid power reliance by incorporating renewable sources into the system. GreenFLag leverages a Soft-Actor Critic reinforcement learning approach to jointly optimize computational and communication resources, while accounting for communication contention and the dynamic availability of renewable energy. Evaluations using a real-world open dataset from Copernicus, demonstrate that GreenFLag significantly reduces grid energy consumption by 94.8% on average, compared to three state-of-the-art baselines, while primarily relying on green power.