FAL Framework

Federated Adversarial Learning framework for running federated learning experiments with clean, adversarial, and mixed training modes.

Install

pip install fal-framework

Use

from sklearn.linear_model import LogisticRegression
from fal import FAL

fal = FAL(
    dataset="wine",
    model=LogisticRegression(max_iter=1000),
    trainer_type="clean",
    metrics=["accuracy", "f1"],
    num_clients=3,
    num_rounds=3,
    fl_algorithm="fedavg",
)

result = fal.run()

Metadata

Release files for fal-framework 0.2.2

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Source distribution for fal-framework 0.2.2
File	Size	Uploaded	Provenance
fal_framework-0.2.2.tar.gz	67.8 kB	Oct 23, 2026	Without provenance	Details

Built distribution (wheel)

Table of built distributions (wheels) for fal-framework 0.2.2
File	Interpreter	ABI	Platform	Reset
fal_framework-0.2.2-py3-none-any.whl 78.2 kB Oct 23, 2026 Without provenance	Python 3	none	any	Details

Total release size: 146.0 kB

Release files / fal_framework-0.2.2.tar.gz

Download URL	fal_framework-0.2.2.tar.gz
Size	67.8 kB
Tags	Source
SHA-256 checksum How to use checksums	`ef4ab708fd2475c4b8c507e7e941484d9619a93d9ccf70de170365bc1794b4ec`
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Uploaded via	`twine/6.2.0 CPython/3.11.9`

Release files / fal_framework-0.2.2-py3-none-any.whl

Download URL	fal_framework-0.2.2-py3-none-any.whl
Size	78.2 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`ea955e600912321168da1b30c63c29b0f37874dc63c7b15c256b4d7c2a8a2e61`
BLAKE2b-256 checksum How to use checksums	`eb741d71239eb2f973c02753515925d81ebd3901464ae6fe3f8868496498e65a`
Upload date	Oct 23, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.11.9`

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