Summary of Prioritizing Modalities: Flexible Importance Scheduling in Federated Multimodal Learning, by Jieming Bian et al.
Prioritizing Modalities: Flexible Importance Scheduling in Federated Multimodal Learning
by Jieming Bian, Lei Wang, Jie Xu
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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 The proposed FlexMod approach enhances computational efficiency in Multimodal Federated Learning (MFL) by adaptively allocating training resources for each modality encoder based on their importance and training requirements. This is achieved through prototype learning, Shapley values, and the Deep Deterministic Policy Gradient (DDPG) method from deep reinforcement learning. The method prioritizes critical modalities, optimizing model performance and resource utilization. Experimental results on three real-world datasets demonstrate that FlexMod significantly improves the performance of MFL models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning is a way for devices to work together to train models without sharing their data. This helps keep user information private and lets devices with limited resources contribute. Most research focuses on using this method with one type of data, but what about when you have different types of data? Multimodal Federated Learning (MFL) tries to solve this problem by letting each type of data use its own special way of processing the data. The challenge is that devices with limited resources might not be able to do as much work on all the different types of data. The proposed FlexMod approach tries to fix this by giving more importance to the most important types of data and less to the others. This makes it more efficient for devices with limited resources to contribute. |
Keywords
» Artificial intelligence » Encoder » Federated learning » Reinforcement learning
