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.

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。