H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications

arXiv:2412.06210 · cs.LG · Submitted 2024-12-09 · Read on arXiv

cs.LG

Submitted: 2024-12-09

Updated: 2026-08-30

Terminology

• Accuracy — 49× in this paper · explained in 1 episode
• Aggregation — 35× in this paper · explained in 1 episode
• Federated Learning — 35× in this paper · explained in 14 episodes
• Communication Overhead — 8× in this paper · explained in 2 episodes
• Non-IID Data — 7× in this paper · explained in 2 episodes
• Data Heterogeneity — 6× in this paper · explained in 3 episodes
• Personalization — 6× in this paper · explained in 2 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Internet of Things — 4× in this paper
• Robustness — 4× in this paper · explained in 5 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Federated Learning (FL) — 2× in this paper · explained in 7 episodes
• International Joint Conference on Neural Networks — 2× in this paper
• Meta-learning — 2× in this paper · explained in 2 episodes
• While Hierarchical Federated Learning — 2× in this paper
• Adaptability — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Digital Twin — 1× in this paper · explained in 4 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes

Sources

• Personalized Federated Learning: A Meta-Learning Approach
• Federated Learning with Personalization Layers
• Sparse Communication for Distributed Gradient Descent
• FedMetaMed: Federated Meta-Learning for Personalized Medication in Distributed Healthcare Systems
• Fed-LDR: Federated Local Data-infused Graph Creation with Node-centric Model Refinement
• Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

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