Summary of Federated Learning For Time-series Healthcare Sensing with Incomplete Modalities, by Adiba Orzikulova et al.
Federated Learning for Time-Series Healthcare Sensing with Incomplete Modalities
by Adiba Orzikulova, Jaehyun Kwak, Jaemin Shin, Sung-Ju Lee
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| 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 paper proposes an efficient Federated Learning (FL) training algorithm, FLISM, which can handle incomplete multimodal sensing data from mobile and wearable devices in healthcare applications. FLISM employs three key techniques: modality-invariant representation learning, modality quality-aware aggregation, and global-aligned knowledge distillation. The algorithm is designed to prioritize contributions from clients with higher-quality modality data while reducing local update shifts caused by modality differences. The paper’s extensive experiments on real-world datasets show that FLISM achieves high accuracy and outperforms state-of-the-art methods in handling incomplete modality problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps create a special kind of artificial intelligence, called Federated Learning (FL), which can be used for healthcare purposes like monitoring patients’ health. The problem is that the data from different sensors might not be complete or accurate. To solve this issue, the researchers created an algorithm called FLISM. It does three main things: it helps computers understand information in a way that’s not affected by the type of sensor used, it prioritizes information from sensors that are most reliable, and it makes sure all the computers agree on what’s important. The results show that FLISM is better than other methods at handling incomplete data. |
Keywords
» Artificial intelligence » Federated learning » Knowledge distillation » Representation learning
