From b6e5e75976ac0ffe823cbb282c9e6fb9ed0621bf Mon Sep 17 00:00:00 2001
From: Paul Andrey <paul.andrey@inria.fr>
Date: Tue, 23 Jul 2024 11:31:13 +0200
Subject: [PATCH] Update user-guide on fairness features.
---
docs/user-guide/fairness.md | 22 ++++++++++++++++++----
1 file changed, 18 insertions(+), 4 deletions(-)
diff --git a/docs/user-guide/fairness.md b/docs/user-guide/fairness.md
index eb9d986..0d03d19 100644
--- a/docs/user-guide/fairness.md
+++ b/docs/user-guide/fairness.md
@@ -335,7 +335,7 @@ as follows:
from declearn.fairness.monitor import FairnessMonitorServer
fairness = FairnessMonitorServer(
- f_type="demographic_parity", # choose any fairness definition
+ f_type="demographic_parity", # choose any fairness definition
)
optim = FLOptimConfig.from_params(
@@ -353,12 +353,26 @@ controller. For instance, to use Fed-FairGrad:
from declearn.fairness.fairgrad import FairgradControllerServer
fairness = FairgradControllerServer(
- f_type="demographic_parity", # choose any fairness definition
- eta=0.1, # adjust this based on the SGD learning rate and empirical tuning
- eps=0.0, # change this to configure epsilon-fairness
+ f_type="demographic_parity", # choose any fairness definition
+ eta=0.1, # adjust this based on the SGD learning rate and empirical tuning
+ eps=0.0, # change this to configure epsilon-fairness
)
```
+Equivalently, the choice of fairness controller class and parameters may be
+specified using a configuration dict (that may be parsed from a TOML file):
+
+```python
+fairness = {
+ # mandatory parmaeters:
+ "algorithm": "fairgrad", # name of the algorithm
+ "f_type": "demographic_parity", # name of the group-fairness definition
+ # optional, algorithm-dependent hyper-parameters:
+ "eta": 0.1,
+ "eps": 0.0,
+}
+```
+
Notes:
- `declearn.fairness.core.list_fairness_functions` may be used to review all
--
GitLab