# Asynchronous Multi-Task Learning

arXiv: https://arxiv.org/abs/1609.09563 · HTML: https://mlsanity.com/paper/1609.09563/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2016.

## Built on by (most cited first, 8 of 8)

- [SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks](https://mlsanity.com/paper/2106.02743/) (2021)
- [Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learning](https://mlsanity.com/paper/2103.01422/) (2021)
- [Private Multi-Task Learning: Formulation and Applications to Federated Learning](https://mlsanity.com/paper/2108.12978/) (2021)
- [OO-dMVMT: A Deep Multi-view Multi-task Classification Framework for Real-time 3D Hand Gesture Classification and Segmentation](https://mlsanity.com/paper/2304.05956/) (2023)
- [Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning](https://mlsanity.com/paper/2403.06535/) (2024)
- [Multi-Trigger-Key: Towards Multi-Task Privacy Preserving In Deep Learning](https://mlsanity.com/paper/2110.03106/) (2021)
- [UniFed: A Universal Federation of a Mixture of Highly Heterogeneous Medical Image Classification Tasks](https://mlsanity.com/paper/2408.07075/) (2024)
- [Motivation is Something You Need](https://mlsanity.com/paper/2602.21064/) (2026)

## Abstract (arXiv)

Many real-world machine learning applications involve several learning tasks which are inter-related. For example, in healthcare domain, we need to learn a predictive model of a certain disease for many hospitals. The models for each hospital may be different because of the inherent differences in the distributions of the patient populations. However, the models are also closely related because of the nature of the learning tasks modeling the same disease. By simultaneously learning all the tasks, multi-task learning (MTL) paradigm performs inductive knowledge transfer among tasks to improve the generalization performance. When datasets for the learning tasks are stored at different locations, it may not always be feasible to transfer the data to provide a data-centralized computing environment due to various practical issues such as high data volume and privacy. In this paper, we propose a principled MTL framework for distributed and asynchronous optimization to address the aforementioned challenges. In our framework, gradient update does not wait for collecting the gradient information from all the tasks. Therefore, the proposed method is very efficient when the communication delay is too high for some task nodes. We show that many regularized MTL formulations can benefit from this framework, including the low-rank MTL for shared subspace learning. Empirical studies on both synthetic and real-world datasets demonstrate the efficiency and effectiveness of the proposed framework.