|
| | | | |
| | | |
| Stat |
Members: 3665
Articles: 2'599'751
Articles rated: 2609
17 January 2025 |
|
| | | | |
|
Article overview
| |
|
|
Leveraging Learning Metrics for Improved Federated Learning | | Andre Fu
; | | Date: |
1 Sep 2023 | | Abstract: | Currently in the federated setting, no learning schemes leverage the emerging
research of explainable artificial intelligence (XAI) in particular the novel
learning metrics that help determine how well a model is learning. One of these
novel learning metrics is termed ’Effective Rank’ (ER) which measures the
Shannon Entropy of the singular values of a matrix, thus enabling a metric
determining how well a layer is mapping. By joining federated learning and the
learning metric, effective rank, this work will extbf{(1)} give the first
federated learning metric aggregation method extbf{(2)} show that effective
rank is well-suited to federated problems by out-performing baseline Federated
Averaging cite{konevcny2016federated} and extbf{(3)} develop a novel
weight-aggregation scheme relying on effective rank. | | Source: | arXiv, 2309.00257 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
|
| |
|
|
|
REGISTER