Summary of R-sfllm: Jamming Resilient Framework For Split Federated Learning with Large Language Models, by Aladin Djuhera et al.
R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Models
by Aladin Djuhera, Vlad C. Andrei, Xinyang Li, Ullrich J. Mönich, Holger Boche, Walid Saad
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
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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 This paper proposes a solution to improve split federated learning (SFL) over wireless networks by developing a resilient framework called R-SFLLM. The framework leverages wireless sensing data to identify jamming directions-of-arrival and devises an anti-jamming strategy that jointly optimizes beamforming, user scheduling, and resource allocation. The proposed method is evaluated using BERT and RoBERTa models on various natural language processing tasks and datasets, achieving close-to-baseline performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, the authors show that jamming large language model (LLM) word embeddings in SFL can significantly impact the learning process. They provide a physical layer framework for resilient SFL with LLMs over wireless networks, which uses wireless sensing data to identify jamming directions-of-arrival and devise an anti-jamming strategy. |
Keywords
» Artificial intelligence » Bert » Federated learning » Large language model » Natural language processing
