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Knowledge Distillation from Language-Oriented to Emergent Communication for Multi-Agent Remote Control
In this work, we compare emergent communication (EC) built upon multi-agent deep reinforcement learning (MADRL) and language-oriented semantic communication (LSC) empowered by a pre-trained large language model (LLM) using human language.
Towards Semantic Communication Protocols: A Probabilistic Logic Perspective
Classical medium access control (MAC) protocols are interpretable, yet their task-agnostic control signaling messages (CMs) are ill-suited for emerging mission-critical applications.
Communication-Efficient and Personalized Federated Lottery Ticket Learning
The lottery ticket hypothesis (LTH) claims that a deep neural network (i. e., ground network) contains a number of subnetworks (i. e., winning tickets), each of which exhibiting identically accurate inference capability as that of the ground network.
Understanding Uncertainty of Edge Computing: New Principle and Design Approach
Due to the edge's position between the cloud and the users, and the recent surge of deep neural network (DNN) applications, edge computing brings about uncertainties that must be understood separately.
Information Theory Networking and Internet Architecture Information Theory
