# Communication-Efficient Multi-Modal Edge Inference via Uncertainty-Aware Distributed Learning

arXiv: https://arxiv.org/abs/2601.14942 · HTML: https://mlsanity.com/paper/2601.14942/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 43 references that are in the map.
- First posted on arXiv 21 Jan 2026.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [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)
- [Trusted Multi-View Classification with Dynamic Evidential Fusion](https://mlsanity.com/paper/2204.11423/) (2022)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [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)
- [DeepJSCC-f: Deep Joint Source-Channel Coding of Images with Feedback](https://mlsanity.com/paper/1911.11174/) (2019)
- [Towards an Intelligent Edge: Wireless Communication Meets Machine Learning](https://mlsanity.com/paper/1809.00343/) (2018)
- [Modality Distillation with Multiple Stream Networks for Action Recognition](https://mlsanity.com/paper/1806.07110/) (2018)
- [Task-Oriented Multi-User Semantic Communications](https://mlsanity.com/paper/2112.10255/) (2021)
- [Robust Semantic Communications with Masked VQ-VAE Enabled Codebook](https://mlsanity.com/paper/2206.04011/) (2022)
- [Task-Oriented Communication for Multi-Device Cooperative Edge Inference](https://mlsanity.com/paper/2109.00172/) (2021)
- [A Unified Multi-Task Semantic Communication System for Multimodal Data](https://mlsanity.com/paper/2209.07689/) (2022)
- [Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions](https://mlsanity.com/paper/2111.08456/) (2021)
- [Large AI Model Empowered Multimodal Semantic Communications](https://mlsanity.com/paper/2309.01249/) (2023)
- [Deep Learning Enabled Semantic Communications with Speech Recognition and Synthesis](https://mlsanity.com/paper/2205.04603/) (2022)
- [Deep Source-Channel Coding for Sentence Semantic Transmission with HARQ](https://mlsanity.com/paper/2106.03009/) (2021)
- [Rethinking Modern Communication from Semantic Coding to Semantic Communication](https://mlsanity.com/paper/2110.08496/) (2021)
- [Semantic Communication Meets Edge Intelligence](https://mlsanity.com/paper/2202.06471/) (2022)
- [Robust Semantic Communications Against Semantic Noise](https://mlsanity.com/paper/2202.03338/) (2022)
- [A Robust Deep Learning Enabled Semantic Communication System for Text](https://mlsanity.com/paper/2206.02596/) (2022)
- [Semantic Communication with Memory](https://mlsanity.com/paper/2303.12335/) (2023)
- [Physical-layer Adversarial Robustness for Deep Learning-based Semantic Communications](https://mlsanity.com/paper/2305.07220/) (2023)
- [Synchronous Multi-modal Semantic Communication System with Packet-level Coding](https://mlsanity.com/paper/2408.04535/) (2024)
- [Task-Oriented Semantic Communication in Large Multimodal Models-based Vehicle Networks](https://mlsanity.com/paper/2505.02413/) (2025)
- [Remote Training in Task-Oriented Communication: Supervised or Self-Supervised with Fine-Tuning?](https://mlsanity.com/paper/2502.17922/) (2025)
- [Multi-Modal Self-Supervised Semantic Communication](https://mlsanity.com/paper/2503.13940/) (2025)

## Abstract (arXiv)

Semantic communication is emerging as a key enabler for distributed edge intelligence due to its capability to convey task-relevant meaning. However, achieving communication-efficient training and robust inference over wireless links remains challenging. This challenge is further exacerbated for multi-modal edge inference (MMEI) by two factors: 1) prohibitive communication overhead for distributed learning over bandwidth-limited wireless links, due to the multi-modal nature of the system; and 2) limited robustness under varying channels and noisy multi-modal inputs. In this paper, we propose a three-stage communication-aware distributed learning framework to improve training and inference efficiency while maintaining robustness over wireless channels. In Stage~I, devices perform local multi-modal self-supervised learning to obtain shared and modality-specific encoders without device--server exchange, thereby reducing the communication cost. In Stage~II, distributed fine-tuning with centralized evidential fusion calibrates per-modality uncertainty and reliably aggregates features distorted by noise or channel fading. In Stage~III, an uncertainty-guided feedback mechanism selectively requests additional features for uncertain samples, optimizing the communication--accuracy tradeoff in the distributed setting. Experiments on RGB--depth indoor scene classification show that the proposed framework attains higher accuracy with far fewer training communication rounds and remains robust to modality degradation or channel variation, outperforming existing self-supervised and fully supervised baselines.