Multi-level Personalized Federated Learning on Heterogeneous and Long-Tailed Data

• URL: http://arxiv.org/abs/2405.06413v1
• Date: Fri, 10 May 2024 11:52:53 GMT
• Title: Multi-level Personalized Federated Learning on Heterogeneous and Long-Tailed Data
• Authors: Rongyu Zhang, Yun Chen, Chenrui Wu, Fangxin Wang, Bo Li,
• Abstract summary: We introduce an innovative personalized Federated Learning framework, Multi-level Personalized Federated Learning (MuPFL) MuPFL integrates three pivotal modules: Biased Activation Value Dropout (BAVD), Adaptive Cluster-based Model Update (ACMU) and Prior Knowledge-assisted Fine-tuning (PKCF) Experiments on diverse real-world datasets show that MuPFL consistently outperforms state-of-the-art baselines, even under extreme non-i.i.d. and long-tail conditions.
• Score: 10.64629029156029
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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