Summary of F-kans: Federated Kolmogorov-arnold Networks, by Engin Zeydan et al.
F-KANs: Federated Kolmogorov-Arnold Networks
by Engin Zeydan, Cristian J. Vaca-Rubio, Luis Blanco, Roberto Pereira, Marius Caus, Abdullah Aydeger
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Networking and Internet Architecture (cs.NI)
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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 This paper introduces an innovative approach to federated learning (FL) using Kolmogorov-Arnold Networks (KANs) for classification tasks. The authors propose a framework that leverages KAN’s adaptive activation capabilities in a federated setting, aiming to improve classification accuracy while preserving privacy. The study compares the performance of federated KANs (F-KANs) with traditional Multi-Layer Perceptrons (MLPs) on a classification task. The results demonstrate that F-KANs significantly outperform federated MLPs in terms of accuracy, precision, recall, F1 score, and stability, showcasing improved predictive analytics capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper presents a new way to learn from data without sharing it with others. They use special computer networks called Kolmogorov-Arnold Networks (KANs) that can help classify things correctly while keeping the data private. The researchers compared their new approach, which they call Federated KANs (F-KANs), with an old way of doing things called Multi-Layer Perceptrons (MLPs). They found that F-KANs worked much better and could handle complex tasks like classifying images or text more accurately. |
Keywords
» Artificial intelligence » Classification » F1 score » Federated learning » Precision » Recall
