  This paper investigates task-oriented communication for multi-device
cooperative edge inference, where a group of distributed low-end edge devices
transmit the extracted features of local samples to a powerful edge server for
inference. While cooperative edge inference can overcome the limited sensing
capability of a single device, it substantially increases the communication
overhead and may incur excessive latency. To enable low-latency cooperative
inference, we propose a learning-based communication scheme that optimizes
local feature extraction and distributed feature encoding in a task-oriented
manner, i.e., to remove data redundancy and transmit information that is
essential for the downstream inference task rather than reconstructing the data
samples at the edge server. Specifically, we leverage an information bottleneck
(IB) principle to extract the task-relevant feature at each edge device and
adopt a distributed information bottleneck (DIB) framework to formalize a
single-letter characterization of the optimal rate-relevance tradeoff for
distributed feature encoding. To admit flexible control of the communication
overhead, we extend the DIB framework to a distributed deterministic
information bottleneck (DDIB) objective that explicitly incorporates the
representational costs of the encoded features. As the IB-based objectives are
computationally prohibitive for high-dimensional data, we adopt variational
approximations to make the optimization problems tractable. To compensate the
potential performance loss due to the variational approximations, we also
develop a selective retransmission (SR) mechanism to identify the redundancy in
the encoded features of multiple edge devices to attain additional
communication overhead reduction. Extensive experiments evidence that the
proposed task-oriented communication scheme achieves a better rate-relevance
tradeoff than baseline methods.
