Collaborative Fairness in Federated Learning

2020/08/27 by Lingjuan Lyu, Xinyi Xu, Lyu, Lingjuan +3 · 3 citations
Computer Science · Social Sciences · #Distributed #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mobile Crowdsensing and Crowdsourcing #Parallel #Privacy, Security, and Data Protection #Privacy-Preserving Technologies in Data #and Cluster Computing (cs.DC)

paper · pdf · doi:10.48550/arxiv.2008.12161

openalex publication_date 2020/08/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In current deep learning paradigms, local training or the Standalone framework tends to result in overfitting and thus poor generalizability. This problem can be addressed by Distributed or Federated Learning (FL) that leverages a parameter server to aggregate model updates from individual participants. However, most existing Distributed or FL frameworks have overlooked an important aspect of participation: collaborative fairness. In particular, all participants can receive the same or similar models, regardless of their contributions. To address this issue, we investigate the collaborative fairness in FL, and propose a novel Collaborative Fair Federated Learning (CFFL) framework which utilizes reputation to enforce participants to converge to different models, thus achieving fairness without compromising the predictive performance. Extensive experiments on benchmark datasets demonstrate that CFFL achieves high fairness, delivers comparable accuracy to the Distributed framework, and outperforms the Standalone framework.

Citations

• Advances and Open Problems in Federated Learning
• Threats to Federated Learning: A Survey

Cited by

• FeDABoost: Fairness Aware Federated Learning with Adaptive Boosting
• Stochastic Self-Organization in Multi-Agent Systems
• FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning

Related

• Fairness in Federated Learning: Trends, Challenges, and Opportunities
• A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning
• Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing
• A Post-Processing-Based Fair Federated Learning Framework
• FAIRVAR: Fair Federated Learning via Variance Regularization