公平联邦学习能否降低个性化需求？（英文版）

英文标题：Can Fair Federated Learning reduce the need for Personalisation?中文摘要：本论文通过策略提出了 Personalisation-aware Federated Learning (PaFL) 的模型，在语言任务中减少了 50% 的表现不佳客户端的数量，在图像任务中也避免了表现不佳客户端数量翻倍的情况，提高了边缘设备参与联邦学习的效率并有望在未来实验和理论分析上得到更多的应用。英文摘要：Federated Learning (FL) enables training ML models on edge clients withoutsharing data. However, the federated model's performance on local data varies,disincentivising the participation of clients who benefit little from FL. FairFL reduces accuracy disparity by focusi