Federated Learning (FL) enables training ML models on edge clients without sharing data. However, the federated model's performance on local data varies, disincentivising the participation of clients who benefit little from FL. Fair FL reduces accuracy disparity by focusing on clients with higher losses while personalisation locally fine-tunes the model. Personalisation provides a participation incentive when an FL model underperforms relative to one trained locally. For situations where the federated model provides a lower accuracy than a model trained entirely locally by a client, personalisation improves the accuracy of the pre-trained federated weights to be similar to or exceed those of the local client model. This paper evaluates two Fair FL (FFL) algorithms as starting points for personalisation. Our results show that FFL provides no benefit to relative performance in a language task and may double the number of underperforming clients for an image task. Instead, we propose Personalisation-aware Federated Learning (PaFL) as a paradigm that pre-emptively uses personalisation losses during training. Our technique shows a 50% reduction in the number of underperforming clients for the language task while lowering the number of underperforming clients in the image task instead of doubling it. Thus, evidence indicates that it may allow a broader set of devices to benefit from FL and represents a promising avenue for future experimentation and theoretical analysis.

翻译：联邦学习（FL）使得在不共享数据的情况下在边缘客户端上训练机器学习模型成为可能。然而，联邦模型在本地数据上的性能存在差异，这降低了那些从联邦学习中获益较少的客户端的参与积极性。公平联邦学习通过关注损失较高的客户端来减少准确率差异，而个性化则对模型进行本地微调。当联邦模型性能不如本地训练模型时，个性化提供了参与激励。对于联邦模型准确率低于客户端完全本地训练模型的情况，个性化可将预训练的联邦权重提升至与本地客户端模型相似或更高的准确率。本文评估了两种公平联邦学习（FFL）算法作为个性化初始点的效果。结果表明，在语言任务中，FFL对相对性能无益，且可能使图像任务中表现不佳的客户端数量翻倍。为此，我们提出“个性化感知联邦学习”（PaFL）范式，该范式在训练过程中预先采用个性化损失。我们的技术使语言任务中表现不佳的客户端数量减少50%，同时使图像任务中此类客户端数量降低而非翻倍。因此，证据表明，该方法可能使更广泛的设备受益于FL，并代表了未来实验与理论分析的一个有前景的研究方向。