Table 5 Details of specifications.
From: Leveraging federated learning and edge computing for pandemic-resilient healthcare
Sl.No | Parameters | Description |
|---|---|---|
1 | Learning rate | Between 0.9 and \(1 \times {10^{-4}}\) |
2 | Attention layer length | Varies between 32 to 128 |
3 | Optimizer | Adam |
4 | Optimizer width | 0.0005 |
5 | Activation Functions | ReLU, Softmax; |
6 | Stride | 1, 2 |
7 | Filter mask (Convolutional) | 3 x 3 for image 1 x 3 for data |
8 | Batch size | 32 |
9 | Learning goal | \(1 \times {10^{-4}}\) |
10 | Gradient size | \(1 \times {10^{-5}}\) |
11 | Input image size | 416 \(\times\) 416 \(\times\) 3 |
12 | Frame rate | 240 fps |
