Authors

* External authors

Venue

Date

Share

MAS: Towards Resource-Efficient Federated Multiple-Task Learning

Weiming Zhuang

Yonggang Wen*

Shuai Zhang*

Lingjuan Lyu

* External authors

ICCV 2023

2023

Abstract

Federated learning (FL) is an emerging distributed machine learning method that empowers in-situ model training on decentralized edge devices. However, multiple simultaneous FL tasks could overload resource-constrained devices. In this work, we propose the first FL system to effectively coordinate and train multiple simultaneous FL tasks. We first formalize the problem of training simultaneous FL tasks. Then, we present our new approach, MAS (Merge and Split), to optimize the performance of training multiple simultaneous FL tasks. MAS starts by merging FL tasks into an all-in-one FL task with a multi-task architecture. After training for a few rounds, MAS splits the all-in-one FL task into two or more FL tasks by using the affinities among tasks measured during the all-in-one training. It then continues training each split of FL tasks based on model parameters from the all-in-one training. Extensive experiments demonstrate that MAS outperforms other methods while reducing training time by 2x and reducing energy consumption by 40%. We hope this work will inspire the community to further study and optimize training simultaneous FL tasks.

Related Publications

How to Evaluate and Mitigate IP Infringement in Visual Generative AI?

ICML, 2025
Zhenting Wang, Chen Chen, Vikash Sehwag, Minzhou Pan*, Lingjuan Lyu

The popularity of visual generative AI models like DALL-E 3, Stable Diffusion XL, Stable Video Diffusion, and Sora has been increasing. Through extensive evaluation, we discovered that the state-of-the-art visual generative models can generate content that bears a striking r…

Six-CD: Benchmarking Concept Removals for Benign Text-to-image Diffusion Models

CVPR, 2025
Jie Ren, Kangrui Chen, Yingqian Cui, Shenglai Zeng, Hui Liu, Yue Xing, Jiliang Tang, Lingjuan Lyu

Text-to-image (T2I) diffusion models have shown exceptional capabilities in generating images that closely correspond to textual prompts. However, the advancement of T2I diffusion models presents significant risks, as the models could be exploited for malicious purposes, suc…

CO-SPY: Combining Semantic and Pixel Features to Detect Synthetic Images by AI

CVPR, 2025
Siyuan Cheng, Lingjuan Lyu, Zhenting Wang, Xiangyu Zhang, Vikash Sehwag

With the rapid advancement of generative AI, it is now pos-sible to synthesize high-quality images in a few seconds.Despite the power of these technologies, they raise signif-icant concerns regarding misuse. Current efforts to dis-tinguish between real and AI-generated image…

  • HOME
  • Publications
  • MAS: Towards Resource-Efficient Federated Multiple-Task Learning

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.