FairEnergy让边缘联邦学习更节能又公平，按贡献分配资源。
FairEnergy: Contribution-Based Fairness meets Energy Efficiency in Federated Learning
- 根据更新量和压缩比计算贡献分，动态分配通信资源。
- 实验显示能量消耗降低79%，且模型精度更高。
- 适合资源不均的边缘设备协同训练场景。
联邦学习（FL）可在分布式设备间协作训练模型并保护数据隐私。然而，在无线边缘系统中，由于设备资源异构、客户端贡献不均及通信容量有限，平衡能效与公平参与仍具挑战。为此，我们提出FairEnergy，一种兼顾公平性的能耗最小化框架，将同时考虑更新幅度与压缩比率的贡献评分，融入设备选择、带宽分配和压缩等级的联合优化。通过松弛二值选择变量并运用拉格朗日分解处理全局带宽耦合，再对每台设备求解子问题。在非独立同分布（non-IID）数据上的实验表明，相比基线策略，FairEnergy在保持更高模型精度的同时，能耗最多降低79%。
原文摘要 · Abstract (English)
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.
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