Details
| Original language | English |
|---|---|
| Pages (from-to) | 10962-10970 |
| Number of pages | 9 |
| Journal | Proceedings of the AAAI Conference on Artificial Intelligence |
| Volume | 38 |
| Issue number | 10 |
| Publication status | Published - 24 Mar 2024 |
| Event | 38th AAAI Conference on Artificial Intelligence, AAAI 2024 - Vancouver, Canada Duration: 20 Feb 2024 → 27 Feb 2024 |
Abstract
As Federated Learning (FL) gains prominence in distributed machine learning applications, achieving fairness without compromising predictive performance becomes paramount. The data being gathered from distributed clients in an FL environment often leads to class imbalance. In such scenarios, balanced accuracy rather than accuracy is the true representation of model performance. However, most state-of-the-art fair FL methods report accuracy as the measure of performance, which can lead to misguided interpretations of the model’s effectiveness to mitigate discrimination. To the best of our knowledge, this work presents the first attempt towards achieving Pareto-optimal trade-offs between balanced accuracy and fairness in a federated environment (FairTrade). By utilizing multi-objective optimization, the framework negotiates the intricate balance between model’s balanced accuracy and fairness. The framework’s agnostic design adeptly accommodates both statistical and causal fairness notions, ensuring its adaptability across diverse FL contexts. We provide empirical evidence of our framework’s efficacy through extensive experiments on five real-world datasets and comparisons with six baselines. The empirical results underscore the potential of our framework in improving the trade-off between fairness and balanced accuracy in FL applications.
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
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In: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38, No. 10, 24.03.2024, p. 10962-10970.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - FairTrade
T2 - 38th AAAI Conference on Artificial Intelligence, AAAI 2024
AU - Badar, Maryam
AU - Sikdar, Sandipan
AU - Nejdl, Wolfgang
AU - Fisichella, Marco
N1 - Funding Information: This research was partially funded by the European Commission through the xAIM project, agreement No. INEA/CEF/ICT/A2020/2276680.
PY - 2024/3/24
Y1 - 2024/3/24
N2 - As Federated Learning (FL) gains prominence in distributed machine learning applications, achieving fairness without compromising predictive performance becomes paramount. The data being gathered from distributed clients in an FL environment often leads to class imbalance. In such scenarios, balanced accuracy rather than accuracy is the true representation of model performance. However, most state-of-the-art fair FL methods report accuracy as the measure of performance, which can lead to misguided interpretations of the model’s effectiveness to mitigate discrimination. To the best of our knowledge, this work presents the first attempt towards achieving Pareto-optimal trade-offs between balanced accuracy and fairness in a federated environment (FairTrade). By utilizing multi-objective optimization, the framework negotiates the intricate balance between model’s balanced accuracy and fairness. The framework’s agnostic design adeptly accommodates both statistical and causal fairness notions, ensuring its adaptability across diverse FL contexts. We provide empirical evidence of our framework’s efficacy through extensive experiments on five real-world datasets and comparisons with six baselines. The empirical results underscore the potential of our framework in improving the trade-off between fairness and balanced accuracy in FL applications.
AB - As Federated Learning (FL) gains prominence in distributed machine learning applications, achieving fairness without compromising predictive performance becomes paramount. The data being gathered from distributed clients in an FL environment often leads to class imbalance. In such scenarios, balanced accuracy rather than accuracy is the true representation of model performance. However, most state-of-the-art fair FL methods report accuracy as the measure of performance, which can lead to misguided interpretations of the model’s effectiveness to mitigate discrimination. To the best of our knowledge, this work presents the first attempt towards achieving Pareto-optimal trade-offs between balanced accuracy and fairness in a federated environment (FairTrade). By utilizing multi-objective optimization, the framework negotiates the intricate balance between model’s balanced accuracy and fairness. The framework’s agnostic design adeptly accommodates both statistical and causal fairness notions, ensuring its adaptability across diverse FL contexts. We provide empirical evidence of our framework’s efficacy through extensive experiments on five real-world datasets and comparisons with six baselines. The empirical results underscore the potential of our framework in improving the trade-off between fairness and balanced accuracy in FL applications.
UR - http://www.scopus.com/inward/record.url?scp=85189754417&partnerID=8YFLogxK
U2 - 10.1609/aaai.v38i10.28971
DO - 10.1609/aaai.v38i10.28971
M3 - Conference article
AN - SCOPUS:85189754417
VL - 38
SP - 10962
EP - 10970
JO - Proceedings of the AAAI Conference on Artificial Intelligence
JF - Proceedings of the AAAI Conference on Artificial Intelligence
SN - 2159-5399
IS - 10
Y2 - 20 February 2024 through 27 February 2024
ER -