Summary of Federated Learning with Mmd-based Early Stopping For Adaptive Gnss Interference Classification, by Nishant S. Gaikwad and Lucas Heublein and Nisha L. Raichur and Tobias Feigl and Christopher Mutschler and Felix Ott
Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification
by Nishant S. Gaikwad, Lucas Heublein, Nisha L. Raichur, Tobias Feigl, Christopher Mutschler, Felix Ott
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 proposed federated learning approach combines few-shot learning with aggregation of model weights on a global server. It addresses the challenge of managing feature distribution for novel and unbalanced data across devices by introducing a dynamic early stopping method based on representation learning. This method balances out-of-distribution classes using the maximum mean discrepancy of feature embeddings between local and global models. The approach is demonstrated to surpass state-of-the-art techniques in adapting to novel interference classes and multipath scenarios through extensive experiments on four GNSS datasets from two real-world highways and controlled environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to train machine learning models helps devices work together without sharing their data. This “federated learning” allows devices to learn from each other’s experiences while keeping their own data private. The challenge is that different devices have different types of data, which can make it hard for the global model to learn from all the data. To fix this, researchers developed a new method that uses a few examples from each device to help the global model adapt to new and unknown situations. This approach was tested on real-world highway data and showed better results than previous methods. |
Keywords
» Artificial intelligence » Early stopping » Federated learning » Few shot » Machine learning » Representation learning
