Federated learning (FL) offers a privacy-centric distributed learning
framework, enabling model training on individual clients and central
aggregation without necessitating data exchange. Nonetheless, FL
implementations often suffer from non-i.i.d. and long-tailed class
distributions across mobile applications, e.g., autonomous vehicles, which
leads models to overfitting as local training may converge to sub-optimal. In
our study, we explore the impact of data heterogeneity on model bias and
introduce an innovative personalized FL framework, Multi-level Personalized
Federated Learning (MuPFL), which leverages the hierarchical architecture of FL
to fully harness computational resources at various levels. This framework
integrates three pivotal modules: Biased Activation Value Dropout (BAVD) to
mitigate overfitting and accelerate training; Adaptive Cluster-based Model
Update (ACMU) to refine local models ensuring coherent global aggregation; and
Prior Knowledge-assisted Classifier Fine-tuning (PKCF) to bolster
classification and personalize models in accord with skewed local data with
shared knowledge. Extensive experiments on diverse real-world datasets for
image classification and semantic segmentation validate that MuPFL consistently
outperforms state-of-the-art baselines, even under extreme non-i.i.d. and
long-tail conditions, which enhances accuracy by as much as 7.39% and
accelerates training by up to 80% at most, marking significant advancements in
both efficiency and effectiveness.

MuPFL 是一种创新的个性化联邦学习框架，通过多层次结构完全利用不同层次的计算资源，解决数据异质性对模型偏差的影响，缓解过拟合、加速训练，提高分类准确性，并在非独立同分布和长尾条件下取得显著的有效性和效率方面的进展。