In the context of personalized federated learning (FL), the critical
challenge is to balance local model improvement and global model tuning when
the personal and global objectives may not be exactly aligned. Inspired by
Bayesian hierarchical models, we develop a self-aware personalized FL method
where each client can automatically balance the training of its local personal
model and the global model that implicitly contributes to other clients'
training. Such a balance is derived from the inter-client and intra-client
uncertainty quantification. A larger inter-client variation implies more
personalization is needed. Correspondingly, our method uses uncertainty-driven
local training steps and aggregation rule instead of conventional local
fine-tuning and sample size-based aggregation. With experimental studies on
synthetic data, Amazon Alexa audio data, and public datasets such as MNIST,
FEMNIST, CIFAR10, and Sent140, we show that our proposed method can achieve
significantly improved personalization performance compared with the existing
counterparts.

本论文提出了一种自我感知的个性化联邦学习方法，通过采用贝叶斯分层模型，利用不确定性量化来平衡本地模型改进和全局模型调整，使用不确定性驱动的本地训练步骤和聚合规则来实现。通过实验研究，该方法显著提高了个性化性能。