Summary of Personalized Federated Learning For Generative Ai-assisted Semantic Communications, by Yubo Peng et al.
Personalized Federated Learning for Generative AI-Assisted Semantic Communications
by Yubo Peng, Feibo Jiang, Li Dong, Kezhi Wang, Kun Yang
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Information Theory (cs.IT)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers develop a novel approach to semantic communication (SC) that leverages generative artificial intelligence (GAI) models to efficiently transmit semantic information between mobile users (MUs) and base stations. The proposed GAI-assisted SC (GSC) model aims to address the issue of spectrum resource utilization caused by various intelligent applications on MUs. To train the GSC model using local MU data while ensuring privacy and accommodating heterogeneous requirements, the authors introduce personalized semantic federated learning (PSFL). PSFL incorporates personalized local distillation (PLD) and adaptive global pruning (AGP), which enables efficient communication energy reduction. The proposed scheme is numerically evaluated to demonstrate its feasibility and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists create a new way to send important information between mobile devices and base stations using special AI models. This helps solve the problem of running out of space on the airwaves caused by many apps and devices. They also develop a method called personalized semantic federated learning that trains the model using data from individual devices while keeping their data private. This approach is shown to be effective in reducing energy consumption for communication. |
Keywords
» Artificial intelligence » Distillation » Federated learning » Pruning
