Summary of Data Similarity-based One-shot Clustering For Multi-task Hierarchical Federated Learning, by Abdulmoneam Ali et al.
Data Similarity-Based One-Shot Clustering for Multi-Task Hierarchical Federated Learning
by Abdulmoneam Ali, Ahmed Arafa
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); Networking and Internet Architecture (cs.NI); Signal Processing (eess.SP)
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 paper proposes a one-shot clustering algorithm to group users in a hierarchical federated learning setting based on their data similarity. This algorithm enables efficient collaboration and sharing of a common layer representation within the system, overcoming challenges related to privacy concerns, communication overhead, and prior knowledge about learning models or loss function behaviors. The proposed method is validated using datasets such as CIFAR-10 and Fashion MNIST, showing improved accuracy and variance reduction compared to the baseline. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps people learn different things together in a way that makes it easier for them to share information. It uses an algorithm to group people with similar data into teams, which makes sharing easier and more efficient. This is important because it can help keep private information safe and reduce the amount of data that needs to be shared. The paper tests this idea using real-life datasets and shows that it works better than other methods. |
Keywords
» Artificial intelligence » Clustering » Federated learning » Loss function » One shot
