https://github.com/akassharjun/shapleyvaluefl
A pip library for calculating the Shapley Value for computing the marginal contribution of each client in a Federated Learning environment.
https://github.com/akassharjun/shapleyvaluefl
federated-learning game-theory incentive-mechanism machine-learning shapley-value
Last synced: 26 days ago
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A pip library for calculating the Shapley Value for computing the marginal contribution of each client in a Federated Learning environment.
- Host: GitHub
- URL: https://github.com/akassharjun/shapleyvaluefl
- Owner: akassharjun
- License: mit
- Created: 2022-05-11T17:54:14.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-12-10T06:46:18.000Z (over 1 year ago)
- Last Synced: 2025-04-23T02:16:50.488Z (26 days ago)
- Topics: federated-learning, game-theory, incentive-mechanism, machine-learning, shapley-value
- Language: Python
- Homepage:
- Size: 16.6 KB
- Stars: 26
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ShapleyValueFL
A pip library for computing the marginal contribution (Shapley Value) for each client in a Federated Learning environment.
## Table of Content
- [ShapleyValueFL](#shapleyvaluefl)
- [Table of Content](#table-of-content)
- [Brief](#brief)
- [Usage](#usage)
- [Future Work](#future-work)
- [Feedback](#feedback)
## Brief
The Shapley Value is a game theory concept that explores how to equitably distribute rewards and costs among members of a coalition. It is extensively used in incentive mechanisms for Federated Learning to fairly distribute rewards to clients based on their contribution to the system.
Let $v(S)$ where $S\subset N$ is defined as the contribution of the model collaboratively trained by the subset $S$. $N$ is a set of all the participants in the system.
The i-th participant’s Shapley Value $\phi(i)$ is defined as
$$\phi(i) = \sum_{S\subset N \backslash \{i\}} \frac{|S|!(N-|S|-1)!}{|N|!}(v(S\cup \{i\}) - v(S))$$
The marginal contribution of the $i-th$ participant is defined as
$(v(S \cup \{i\}) - v(S))$ when they join this coalition.
Let's see this equation in action, consider a Federated Learning environment with three clients, so $N = \{0, 1, 2\}$. We list the contribution of each subset within this coalition. Let's consider the contribution to be measured in terms of model accuracy.
$v(\emptyset) = 0$ $v(\{0\}) = 40$ $v(\{1\}) = 60$ $v(\{2\}) = 80$
$v(\{0,1\}) = 70$ $v(\{0,2\}) = 75$ $v(\{1,2\}) = 85$
$v(\{0,1,2\}) = 90$
| Subset | Client #0 | Client #1 | Client #2 |
| ------------- | ------------- | ------------- | ------------- |
| $0 \leftarrow 1 \leftarrow 2$ | 40 | 30 | 20 |
| $0 \leftarrow 2 \leftarrow 1$ | 40 | 15 | 35 |
| $1 \leftarrow 0 \leftarrow 2$ | 10 | 60 | 20 |
| $1 \leftarrow 2 \leftarrow 0$ | 5 | 60 | 25 |
| $2 \leftarrow 0 \leftarrow 1$ | 0 | 10 | 80 |
| $2 \leftarrow 1 \leftarrow 0$ | 5 | 5 | 80 |
| $Sum$ | 100 | 180 | 260 |
| $\phi(i)$ | 16.67 | 30 | 43.33 |
The arrow signifies the order in which each client joins the coalition. Consider the
first iteration $0 \leftarrow 1 \leftarrow 2$, we calculate the marginal contribution of each client using the
above equation.
- Client 0's marginal contribution is given as $v(\{0\}) = 40$.
- Client 1's marginal contribution is given as $v(\{0, 1\}) - v(\{0\}) = 30$.
- Client 2's marginal contribution is given as $v(\{0, 1, 2\}) - v(\{0, 1\}) - v(\{0\}) = 20$.
The marginal contribution is calculated for each permutation
likewise, and the Shapley Value is derived by averaging all of these marginal contributions.
## Usage
```python
from svfl.svfl import calculate_sv
models = {
"client-id-1" : ModelUpdate(),
"client-id-2" : ModelUpdate(),
"client-id-3" : ModelUpdate(),
}
def evaluate_model(model):
# function to compute evaluation metric, ex: accuracy, precision
return metric
def fed_avg(models):
# function to merge the model updates into one model for evaluation, ex: FedAvg, FedProx
return model
# returns a key value pair with the client identifier and it's respective Shapley Value
contribution_measure = calculate_sv(models, evaluate_model, fed_avg)
```
## Future Work
- Built-in support for standard averaging methods like FedAvg, & FedProx.
## Feedback
Any feedback/corrections/additions are welcome:
If this was helpful, please leave a star on the [github](https://github.com/akassharjun/ShapleyValueFL) page.