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Large Language Model-Driven Curriculum Design for Mobile Networks
Our framework mitigates this by utilizing the generative capabilities of LLMs to automate the curriculum design process, significantly reducing human effort while improving the RL agent's convergence and performance.
Dynamic Encoding and Decoding of Information for Split Learning in Mobile-Edge Computing: Leveraging Information Bottleneck Theory
Split learning is a privacy-preserving distributed learning paradigm in which an ML model (e. g., a neural network) is split into two parts (i. e., an encoder and a decoder).
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