  Distributed computing in the context of deep neural networks (DNNs) implies
the execution of one part of the network on edge devices and the other part
typically on a large-scale cloud platform. Conventional methods propose to
employ a serial concatenation of a learned image and source encoder, the latter
projecting the image encoder output (bottleneck features) into a quantized
representation for bitrate-efficient transmission. In the cloud, a respective
source decoder reprojects the quantized representation to the original feature
representation, serving as an input for the downstream task decoder performing,
e.g., semantic segmentation. In this work, we propose joint source and task
decoding, as it allows for a smaller network size in the cloud. This further
enables the scalability of such services in large numbers without requiring
extensive computational load on the cloud per channel. We demonstrate the
effectiveness of our method by achieving a distributed semantic segmentation
SOTA over a wide range of bitrates on the mean intersection over union metric,
while using only $9.8 \%$ ... $11.59 \%$ of cloud DNN parameters used in the
previous SOTA on the COCO and Cityscapes datasets.
