Conceitos Básicos
FedSPU enhances personalized federated learning by maintaining local model personalization and overcoming computation bottlenecks.
Resumo
Introduction to Federated Learning (FL) in IoT applications.
Challenges of non-iid data and resource constraints on edge devices.
Comparison of Federated Dropout and proposed FedSPU.
Detailed explanation of FedSPU's approach and benefits.
Experimental results showing FedSPU outperforming existing methods.
Estatísticas
実験結果は、FedSPUが平均で精度を7.57%向上させることを示しています。
早期停止スキームにより、トレーニング時間が24.8%〜70.4%削減されました。