Summary of Communication-efficient Training Workload Balancing For Decentralized Multi-agent Learning, by Seyed Mahmoud Sajjadi Mohammadabadi et al.
Communication-Efficient Training Workload Balancing for Decentralized Multi-Agent Learning
by Seyed Mahmoud Sajjadi Mohammadabadi, Lei Yang, Feng Yan, Junshan Zhang
First submitted to arxiv on: 1 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Multiagent Systems (cs.MA); Performance (cs.PF)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents ComDML, a decentralized multi-agent learning (DML) framework that tackles heterogeneity in agents’ resources to minimize training time. The proposed approach, Communication-Efficient Training Workload Balancing for Decentralized Multi-Agent Learning (ComDML), leverages local-loss split training and integer programming to optimize workload balancing among agents. ComDML enables parallel updates by offloading part of slower agents’ workloads to faster agents, considering both communication and computation capacities. Experimental results on CIFAR-10, CIFAR-100, and CINIC-10 datasets with ResNet-56 and ResNet-110 models demonstrate significant reductions in overall training time while maintaining model accuracy, outperforming state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a team of robots or computers working together to learn from data. Each one has different strengths and weaknesses, which can cause problems when they’re all learning at the same time. This paper presents a new way for these agents to work together efficiently, without slowing each other down. The approach uses special techniques to make sure everyone contributes equally, even if some are faster or slower than others. By doing so, it saves time and makes the learning process more accurate. The results show that this new method can be very effective in a variety of situations. |
Keywords
» Artificial intelligence » Resnet
