FairEnergy: Contribution-Based Fairness meets Energy Efficiency in Federated Learning
This work addresses energy and fairness issues in federated learning for edge computing, offering an incremental improvement over existing methods.
The paper tackles the challenge of balancing energy efficiency, fair participation, and high model accuracy in federated learning for wireless edge systems by proposing FairEnergy, a framework that integrates contribution scores into joint optimization, achieving up to 79% energy reduction while maintaining higher accuracy compared to baselines.
Federated learning (FL) enables collaborative model training across distributed devices while preserving data privacy. However, balancing energy efficiency and fair participation while ensuring high model accuracy remains challenging in wireless edge systems due to heterogeneous resources, unequal client contributions, and limited communication capacity. To address these challenges, we propose FairEnergy, a fairness-aware energy minimization framework that integrates a contribution score capturing both the magnitude of updates and their compression ratio into the joint optimization of device selection, bandwidth allocation, and compression level. The resulting mixed-integer non-convex problem is solved by relaxing binary selection variables and applying Lagrangian decomposition to handle global bandwidth coupling, followed by per-device subproblem optimization. Experiments on non-IID data show that FairEnergy achieves higher accuracy while reducing energy consumption by up to 79\% compared to baseline strategies.