Summary of Fedmac: Tackling Partial-modality Missing in Federated Learning with Cross-modal Aggregation and Contrastive Regularization, by Manh Duong Nguyen et al.
FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization
by Manh Duong Nguyen, Trung Thanh Nguyen, Huy Hieu Pham, Trong Nghia Hoang, Phi Le Nguyen, Thanh Trung Huynh
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multimedia (cs.MM)
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 Federated Learning (FL) enables collaborative model training across distributed data sources while preserving privacy. A significant challenge arises when dealing with missing modalities in clients’ datasets, leading to heterogeneous data distribution. Existing studies focus on complete-modality missing but neglect partial-modality missing due to instance-level heterogeneity. To address this challenge, the proposed FedMAC framework tackles multi-modality missing under conditions of partial-modality missing in FL. Contrastive-based regularization is introduced to impose constraints on the latent representation space. Experimental results demonstrate the effectiveness of FedMAC across various client configurations with statistical heterogeneity, outperforming baseline methods by up to 26% in severe missing scenarios. This study highlights the potential of FedMAC as a solution for partially missing modalities in federated systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning is a way to train machine learning models using data from many sources. The problem is when some sources don’t have all the information, making it hard to work together. Existing solutions focus on when everything is present, but this study looks at when only some parts are missing. It proposes a new approach called FedMAC that can handle this challenge. To make sure the results are good, the study uses special rules to control how the data is processed. The results show that FedMAC works well in different situations and does better than other methods by up to 26%. This could be important for building reliable systems that work together. |
Keywords
» Artificial intelligence » Federated learning » Machine learning » Regularization
