# Multi-task Domain Adaptation for Computation Offloading in Edge-intelligence Networks

arXiv: https://arxiv.org/abs/2501.07585 · HTML: https://mlsanity.com/paper/2501.07585/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 2 Jan 2025.

## Builds on (most cited first, 4 of 4)

- [Continual Test-Time Domain Adaptation](https://mlsanity.com/paper/2203.13591/) (2022)
- [Edge Intelligence for Autonomous Driving in 6G Wireless System: Design Challenges and Solutions](https://mlsanity.com/paper/2012.06992/) (2020)
- [Lyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2305.10931/) (2023)
- [Multi-hop RIS-aided Learning Model Sharing for Urban Air Mobility](https://mlsanity.com/paper/2411.06015/) (2024)

## Abstract (arXiv)

In the field of multi-access edge computing (MEC), efficient computation offloading is crucial for improving resource utilization and reducing latency in dynamically changing environments. This paper introduces a new approach, termed as Multi-Task Domain Adaptation (MTDA), aiming to enhance the ability of computational offloading models to generalize in the presence of domain shifts, i.e., when new data in the target environment significantly differs from the data in the source domain. The proposed MTDA model incorporates a teacher-student architecture that allows continuous adaptation without necessitating access to the source domain data during inference, thereby maintaining privacy and reducing computational overhead. Utilizing a multi-task learning framework that simultaneously manages offloading decisions and resource allocation, the proposed MTDA approach outperforms benchmark methods regarding mean squared error and accuracy, particularly in environments with increasing numbers of users. It is observed by means of computer simulation that the proposed MTDA model maintains high performance across various scenarios, demonstrating its potential for practical deployment in emerging MEC applications.