| --- |
| license: apache-2.0 |
| library_name: swarmfl |
| tags: |
| - federated-learning |
| - flower |
| - multi-agent-systems |
| - tabular-classification |
| - research |
| --- |
| |
| # SwarmFL |
|
|
| Small Flower demo for authorized specialist consultation. Synthetic only; not a |
| benchmark or clinical model. |
|
|
| ## Quickstart |
|
|
| ```bash |
| pip install swarmfl==0.1.0 |
| swarmfl-quickstart --output demo --feedback |
| python -c "import json; print(json.load(open('demo/result.json')))" |
| ``` |
|
|
| The command starts three local Flower nodes, registers their declarations, performs |
| a selected one-peer consultation on a new case, and writes the fused probability |
| and audit artifacts under `demo/`. The synthetic specialists use the ordered input |
| views `(x0,x1)`, `(x2,x3)`, and `(x4,x5)`. |
|
|
| The demo keeps specialist views separate, makes one selected peer request, and |
| fuses the observed predictions. |
|
|
| ```bash |
| python demo.py --feedback |
| ``` |
|
|
| If needed, install the package first: |
|
|
| ```bash |
| pip install swarmfl==0.1.0 |
| ``` |
|
|
| The public Hub release contains no research datasets, frozen tests, private |
| checkpoints, or experiment outputs. See the [repository](https://github.com/saadbinather/SwarmFL) |
| for source and research context. |
|
|
