Multi-Task-Oriented Broadcast for Edge AI Inference via Information Bottleneck

In this paper, we consider a task-oriented communication paradigm over multi-user broadcast channels for multi-task edge AI inference, where an edge transmitter performs feature extraction and broadcasts the encoded features to multiple edge devices, each of which conducts a specific inference task based on the received signals. However, due to the heterogeneity of communication links, it still remains an open problem to balance the trade-off between inference accuracy and robustness to channel noise over broadcast channels. To address this issue, we propose a task-oriented broadcast framework guided by information bottleneck (IB) for feature extraction and broadcasting, yielding a two-phase network training strategy, where the former aims to extract task-relevant features for each task, and the latter is utilized to compress the extracted features and perform robust broadcasting. Simulation results demonstrate that the proposed task-oriented broadcast framework can achieve a better trade-off between inference accuracy and robustness under various channel conditions than the benchmarks.