FAIR-FATE: Fair Federated Learning with Momentum
暂无分享,去创建一个
[1] Cyprien de Lichy,et al. Towards Multi-Objective Statistically Fair Federated Learning , 2022, ArXiv.
[2] Kangwook Lee,et al. Improving Fairness via Federated Learning , 2021, ArXiv.
[3] Yahya H. Ezzeldin,et al. FairFed: Enabling Group Fairness in Federated Learning , 2021, AAAI.
[4] Sahil Verma,et al. Removing biased data to improve fairness and accuracy , 2021, ArXiv.
[5] Ziyi Kou,et al. FairFL: A Fair Federated Learning Approach to Reducing Demographic Bias in Privacy-Sensitive Classification Models , 2020, 2020 IEEE International Conference on Big Data (Big Data).
[6] Heiko Ludwig,et al. Mitigating Bias in Federated Learning , 2020, ArXiv.
[7] Michele Loi,et al. On the Moral Justification of Statistical Parity , 2020, FAccT.
[8] Christian Haas,et al. Fairness in Machine Learning: A Survey , 2020, ACM Comput. Surv..
[9] Alan Mishler,et al. Fairness in Risk Assessment Instruments: Post-Processing to Achieve Counterfactual Equalized Odds , 2020, FAccT.
[10] L. N. Vicente,et al. Accuracy and fairness trade-offs in machine learning: a stochastic multi-objective approach , 2020, Computational Management Science.
[11] Seong Joon Oh,et al. AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights , 2020, ICLR.
[12] Erez Shmueli,et al. Algorithmic Fairness , 2020, ArXiv.
[13] Hanna M. Wallach,et al. Measurement and Fairness , 2019, FAccT.
[14] Anastasios Kyrillidis,et al. Demon: Improved Neural Network Training With Momentum Decay , 2019, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[15] Li Chen,et al. Accelerating Federated Learning via Momentum Gradient Descent , 2019, IEEE Transactions on Parallel and Distributed Systems.
[16] Tzu-Ming Harry Hsu,et al. Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification , 2019, ArXiv.
[17] Kristina Lerman,et al. A Survey on Bias and Fairness in Machine Learning , 2019, ACM Comput. Surv..
[18] Pranjal Awasthi,et al. Equalized odds postprocessing under imperfect group information , 2019, AISTATS.
[19] Berk Ustun,et al. Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions , 2019, ICML.
[20] Prateek Mittal,et al. Analyzing Federated Learning through an Adversarial Lens , 2018, ICML.
[21] Sharad Goel,et al. The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning , 2018, ArXiv.
[22] Julia Rubin,et al. Fairness Definitions Explained , 2018, 2018 IEEE/ACM International Workshop on Software Fairness (FairWare).
[23] Suresh Venkatasubramanian,et al. A comparative study of fairness-enhancing interventions in machine learning , 2018, FAT.
[24] Nisheeth K. Vishnoi,et al. How to be Fair and Diverse? , 2016, ArXiv.
[25] Nathan Srebro,et al. Equality of Opportunity in Supervised Learning , 2016, NIPS.
[26] Blaise Agüera y Arcas,et al. Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.
[27] Krishna P. Gummadi,et al. Fairness Constraints: Mechanisms for Fair Classification , 2015, AISTATS.
[28] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[29] Carlos Eduardo Scheidegger,et al. Certifying and Removing Disparate Impact , 2014, KDD.
[30] F. Kamiran,et al. Data preprocessing techniques for classification without discrimination , 2012, Knowledge and Information Systems.
[31] Jun Sakuma,et al. Fairness-Aware Classifier with Prejudice Remover Regularizer , 2012, ECML/PKDD.
[32] Toon Calders,et al. Three naive Bayes approaches for discrimination-free classification , 2010, Data Mining and Knowledge Discovery.
[33] Samhita Kanaparthy,et al. Fair Federated Learning for Heterogeneous Face Data , 2021, ArXiv.
[34] Bernardete Ribeiro,et al. Decay Momentum for Improving Federated Learning , 2021, ESANN.
[35] Vaikkunth Mugunthan,et al. Bias-Free FedGAN: A Federated Approach to Generate Bias-Free Datasets , 2021 .
[36] Miriam Seoane Santos,et al. FAWOS: Fairness-Aware Oversampling Algorithm Based on Distributions of Sensitive Attributes , 2021, IEEE Access.
[37] Ning Qian,et al. On the momentum term in gradient descent learning algorithms , 1999, Neural Networks.
[38] Linda F. Wightman. LSAC National Longitudinal Bar Passage Study. LSAC Research Report Series. , 1998 .
[39] Boris Polyak. Some methods of speeding up the convergence of iteration methods , 1964 .