Details

Autor(en) / Beteiligte

• Xu, Yang
• Liao, Yunming
• Wang, Lun
• Xu, Hongli
• Jiang, Zhida
• Zhang, Wuyang

Titel

Overcoming Noisy Labels and Non-IID Data in Edge Federated Learning

Ist Teil von

• IEEE transactions on mobile computing, 2024, p.1-16

Ort / Verlag

IEEE

Erscheinungsjahr

2024

Quelle

IEL

Beschreibungen/Notizen

• Federated learning (FL) enables edge devices to cooperatively train models without exposing their raw data. However, implementing a practical FL system at the network edge mainly faces three challenges: label noise, data non-IIDness, and device heterogeneity, which seriously harm model performance and slow down convergence speed. Unfortunately, none of the existing works tackle all three challenges simultaneously. To this end, we develop a novel FL system, called Aorta, which features adaptive d a taset c o nstruction and agg r egation weigh t a ssignment. On each client, Aorta first calibrates potentially noisy labels and then constructs a training dataset with low noise, balanced distribution, and proper size. To fully utilize limited data on clients, we propose a global model guided method to select clean data and progressively correct noisy labels. To achieve balanced class distribution and proper dataset size, we propose a distribution-and-capability-aware data augmentation method to generate local training data. On the server, Aorta assigns aggregation weights based on the quality of local models to ensure that high-quality models have a greater influence on the global model. The model quality is measured through its cosine similarity with a benchmark model, which is trained on a clean and balanced dataset. We conduct extensive experiments on four datasets with various settings, including different noise types/ratios and non-IID types/levels. Compared to the baselines, Aorta improves model accuracy up to 9.8% on the datasets with moderate noise and non-IIDness, while providing a speedup of 4.2× on average when achieving the same target accuracy.