公平的客户端选择会牺牲训练速度，但在波动环境中更均衡。
Edge AI in Highly Volatile Environments: Is Fairness Worth the Accuracy Trade-off?
- 采用公平算法选择客户端，避免资源强的主导训练
- 公平策略使训练慢15%-28%，但参与机会更均等
- 适合关注隐私与公平的边缘计算场景
联邦学习（FL）已成为边缘智能的变革性范式，可在保护数据隐私的同时实现分布式设备协同训练。然而，边缘环境固有的不稳定性，如资源动态变化和客户端能力异构，给实现高精度与公平性带来了挑战。本文深入评估了基于公平性的客户端选择算法（如RBFF、RBCSF）在三个基准数据集（CIFAR10、FashionMNIST、EMNIST）中，相对于随机与贪婪选择在公平性、模型性能和训练时间上的表现。结果表明，在高度波动的边缘环境中，更公平的客户端选择虽能提升各客户端参与机会的均衡性，但会导致全局训练速度下降15%–28%。本研究揭示了公平性-性能与公平性-速度之间的权衡，并为未来公平客户端选择策略的研究提供了方向。
原文摘要 · Abstract (English)
Federated learning (FL) has emerged as a transformative paradigm for edge intelligence, enabling collaborative model training while preserving data privacy across distributed personal devices. However, the inherent volatility of edge environments, characterized by dynamic resource availability and heterogeneous client capabilities, poses significant challenges for achieving high accuracy and fairness in client participation. This paper investigates the fundamental trade-off between model accuracy and fairness in highly volatile edge environments. This paper provides an extensive empirical evaluation of fairness-based client selection algorithms such as RBFF and RBCSF against random and greedy client selection regarding fairness, model performance, and time, in three benchmarking datasets (CIFAR10, FashionMNIST, and EMNIST). This work aims to shed light on the fairness-performance and fairness-speed trade-offs in a volatile edge environment and explore potential future research opportunities to address existing pitfalls in \textit{fair client selection} strategies in FL. Our results indicate that more equitable client selection algorithms, while providing a marginally better opportunity among clients, can result in slower global training in volatile environments\footnote{The code for our experiments can be found at https://github.com/obaidullahzaland/FairFL_FLTA.
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