FWFA: Fairness-Weighted Federated Aggregation for Privacy-Aware Decision Intelligence
Ensuring fairness in automated decision-making is a critical challenge, especially in organizational contexts like recruitment, performance evaluation, and promotion. As machine learning (ML) and artificial intelligence (AI) increasingly influence such decisions, promoting responsible AI that minimi...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11075742/ |
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| author | Rahul Haripriya Nilay Khare Manish Pandey Shrijal Patel Jaytrilok Choudhary Dhirendra Pratap Singh Surendra Solanki Duansh Sharma |
| author_facet | Rahul Haripriya Nilay Khare Manish Pandey Shrijal Patel Jaytrilok Choudhary Dhirendra Pratap Singh Surendra Solanki Duansh Sharma |
| author_sort | Rahul Haripriya |
| collection | DOAJ |
| description | Ensuring fairness in automated decision-making is a critical challenge, especially in organizational contexts like recruitment, performance evaluation, and promotion. As machine learning (ML) and artificial intelligence (AI) increasingly influence such decisions, promoting responsible AI that minimizes bias while preserving data privacy has become essential. However, existing fairness-aware models are often centralized or ill-equipped to handle non-IID data, limiting their real-world applicability. This study introduces a novel federated learning framework, Fairness-Weighted Federated Aggregation (FWFA), which integrates fairness-aware weighting into the model aggregation process. Each client’s contribution is scaled using a fairness score computed from key metrics Demographic Parity (DP), Statistical Parity Difference (SPD), and Disparate Impact Ratio (DIR). A synthetically generated dataset simulating diverse employee profiles across five professional domains was used to replicate real-world heterogeneity and imbalance. Across 20 communication rounds, FWFA achieved a DP of 0.91, an SPD of 0.06, and a DIR of 0.91, outperforming baseline methods WA+FL and SMOTE+FL, while maintaining an accuracy of 0.84. Additionally, a dynamic weighting mechanism was simulated by varying fairness thresholds to explore adaptive aggregation behavior, revealing a controllable trade-off between fairness and model performance. To further strengthen privacy guarantees, differential privacy was integrated into the FWFA framework, resulting in minimal performance degradation while retaining key fairness properties. These findings reinforce FWFA’s role as a robust, privacy-preserving solution for fair collaborative decision-making in federated environments, supporting the broader vision of ethical and trustworthy AI in real-world systems. |
| format | Article |
| id | doaj-art-b79c63df9cde4ddba38de75cf7e8ddcb |
| institution | Matheson Library |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b79c63df9cde4ddba38de75cf7e8ddcb2025-07-17T23:01:46ZengIEEEIEEE Access2169-35362025-01-011312041712043610.1109/ACCESS.2025.358734411075742FWFA: Fairness-Weighted Federated Aggregation for Privacy-Aware Decision IntelligenceRahul Haripriya0https://orcid.org/0009-0006-3510-7758Nilay Khare1Manish Pandey2Shrijal Patel3Jaytrilok Choudhary4Dhirendra Pratap Singh5Surendra Solanki6https://orcid.org/0000-0002-5067-7621Duansh Sharma7Department of Computer Science and Engineering, Maulana Azad National Institute of Technology Bhopal, Bhopal, IndiaDepartment of Computer Science and Engineering, Maulana Azad National Institute of Technology Bhopal, Bhopal, IndiaDepartment of Computer Science and Engineering, Maulana Azad National Institute of Technology Bhopal, Bhopal, IndiaDepartment of Computer Science and Engineering, Maulana Azad National Institute of Technology Bhopal, Bhopal, IndiaDepartment of Computer Science and Engineering, Maulana Azad National Institute of Technology Bhopal, Bhopal, IndiaDepartment of Computer Science and Engineering, Maulana Azad National Institute of Technology Bhopal, Bhopal, IndiaDepartment of Artificial Intelligence and Machine Learning, Manipal University Jaipur, Jaipur, Rajasthan, IndiaSchool of Arts and Sciences, Rutgers University, New Brunswick, NJ, USAEnsuring fairness in automated decision-making is a critical challenge, especially in organizational contexts like recruitment, performance evaluation, and promotion. As machine learning (ML) and artificial intelligence (AI) increasingly influence such decisions, promoting responsible AI that minimizes bias while preserving data privacy has become essential. However, existing fairness-aware models are often centralized or ill-equipped to handle non-IID data, limiting their real-world applicability. This study introduces a novel federated learning framework, Fairness-Weighted Federated Aggregation (FWFA), which integrates fairness-aware weighting into the model aggregation process. Each client’s contribution is scaled using a fairness score computed from key metrics Demographic Parity (DP), Statistical Parity Difference (SPD), and Disparate Impact Ratio (DIR). A synthetically generated dataset simulating diverse employee profiles across five professional domains was used to replicate real-world heterogeneity and imbalance. Across 20 communication rounds, FWFA achieved a DP of 0.91, an SPD of 0.06, and a DIR of 0.91, outperforming baseline methods WA+FL and SMOTE+FL, while maintaining an accuracy of 0.84. Additionally, a dynamic weighting mechanism was simulated by varying fairness thresholds to explore adaptive aggregation behavior, revealing a controllable trade-off between fairness and model performance. To further strengthen privacy guarantees, differential privacy was integrated into the FWFA framework, resulting in minimal performance degradation while retaining key fairness properties. These findings reinforce FWFA’s role as a robust, privacy-preserving solution for fair collaborative decision-making in federated environments, supporting the broader vision of ethical and trustworthy AI in real-world systems.https://ieeexplore.ieee.org/document/11075742/Federated learningresponsible AIdecision makingmachine learningdata miningcollaborative AI |
| spellingShingle | Rahul Haripriya Nilay Khare Manish Pandey Shrijal Patel Jaytrilok Choudhary Dhirendra Pratap Singh Surendra Solanki Duansh Sharma FWFA: Fairness-Weighted Federated Aggregation for Privacy-Aware Decision Intelligence IEEE Access Federated learning responsible AI decision making machine learning data mining collaborative AI |
| title | FWFA: Fairness-Weighted Federated Aggregation for Privacy-Aware Decision Intelligence |
| title_full | FWFA: Fairness-Weighted Federated Aggregation for Privacy-Aware Decision Intelligence |
| title_fullStr | FWFA: Fairness-Weighted Federated Aggregation for Privacy-Aware Decision Intelligence |
| title_full_unstemmed | FWFA: Fairness-Weighted Federated Aggregation for Privacy-Aware Decision Intelligence |
| title_short | FWFA: Fairness-Weighted Federated Aggregation for Privacy-Aware Decision Intelligence |
| title_sort | fwfa fairness weighted federated aggregation for privacy aware decision intelligence |
| topic | Federated learning responsible AI decision making machine learning data mining collaborative AI |
| url | https://ieeexplore.ieee.org/document/11075742/ |
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