# Multi-Receiver Task-Oriented Communications via Multi-Task Deep Learning

arXiv: https://arxiv.org/abs/2308.06884 · HTML: https://mlsanity.com/paper/2308.06884/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 14 Aug 2023.

## Builds on (most cited first, 15 of 15)

- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach](https://mlsanity.com/paper/2102.04170/) (2021)
- [Semantic Communication Systems for Speech Transmission](https://mlsanity.com/paper/2102.12605/) (2021)
- [Semantic Communications: Principles and Challenges](https://mlsanity.com/paper/2201.01389/) (2021)
- [Less Data, More Knowledge: Building Next Generation Semantic Communication Networks](https://mlsanity.com/paper/2211.14343/) (2022)
- [Wireless Semantic Communications for Video Conferencing](https://mlsanity.com/paper/2204.07790/) (2022)
- [Deep Learning for Wireless Communications](https://mlsanity.com/paper/2005.06068/) (2020)
- [Task-Oriented Image Transmission for Scene Classification in Unmanned Aerial Systems](https://mlsanity.com/paper/2112.10948/) (2021)
- [Task-Oriented Communications for NextG: End-to-End Deep Learning and AI Security Aspects](https://mlsanity.com/paper/2212.09668/) (2022)
- [Is Semantic Communications Secure? A Tale of Multi-Domain Adversarial Attacks](https://mlsanity.com/paper/2212.10438/) (2022)
- [Vulnerabilities of Deep Learning-Driven Semantic Communications to Backdoor (Trojan) Attacks](https://mlsanity.com/paper/2212.11205/) (2022)
- [FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning](https://mlsanity.com/paper/2203.13663/) (2022)
- [Age of Information in Deep Learning-Driven Task-Oriented Communications](https://mlsanity.com/paper/2301.04298/) (2023)
- [Personalized Decentralized Multi-Task Learning Over Dynamic Communication Graphs](https://mlsanity.com/paper/2212.11268/) (2022)

## Built on by (most cited first, 5 of 5)

- [Semantic Communication for Cooperative Multi-Task Processing over Wireless Networks](https://mlsanity.com/paper/2404.08483/) (2024)
- [Cooperative and Collaborative Multi-Task Semantic Communication for Distributed Sources](https://mlsanity.com/paper/2411.02150/) (2024)
- [Low-Latency Task-Oriented Communications with Multi-Round, Multi-Task Deep Learning](https://mlsanity.com/paper/2411.10385/) (2024)
- [Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learning](https://mlsanity.com/paper/2412.21164/) (2024)
- [Semantic-Aware Task Clustering for Constructive and Cooperative Multi-Tasking](https://mlsanity.com/paper/2607.21426/) (2026)

## Abstract (arXiv)

This paper studies task-oriented, otherwise known as goal-oriented, communications, in a setting where a transmitter communicates with multiple receivers, each with its own task to complete on a dataset, e.g., images, available at the transmitter. A multi-task deep learning approach that involves training a common encoder at the transmitter and individual decoders at the receivers is presented for joint optimization of completing multiple tasks and communicating with multiple receivers. By providing efficient resource allocation at the edge of 6G networks, the proposed approach allows the communications system to adapt to varying channel conditions and achieves task-specific objectives while minimizing transmission overhead. Joint training of the encoder and decoders using multi-task learning captures shared information across tasks and optimizes the communication process accordingly. By leveraging the broadcast nature of wireless communications, multi-receiver task-oriented communications (MTOC) reduces the number of transmissions required to complete tasks at different receivers. Performance evaluation conducted on the MNIST, Fashion MNIST, and CIFAR-10 datasets (with image classification considered for different tasks) demonstrates the effectiveness of MTOC in terms of classification accuracy and resource utilization compared to single-task-oriented communication systems.