{"type":"ScholarlyArticle","title":"EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning","authors":[{"name":"Syed Irfan Ali Meerza","slug":"syed-irfan-ali-meerza","given":"Syed Irfan Ali","family":"Meerza","affiliation":"University of Tennessee , Knoxville"},{"name":"Jian Liu","slug":"jian-liu","given":"Jian","family":"Liu","affiliation":"University of Tennessee , Knoxville"}],"abstract":"Federated Learning (FL) is a technique that allows multiple parties to train a shared model collaboratively without disclosing their private data. It has become increasingly popular due to its distinct privacy advantages. However, FL models can suffer from biases against certain demographic groups (e.g., racial and gender groups) due to the heterogeneity of data and party selection. Researchers have proposed various strategies for characterizing the group fairness of FL algorithms to address this issue. However, the effectiveness of these strategies in the face of deliberate adversarial attacks has not been fully explored. Although existing studies have revealed various threats (e.g., model poisoning attacks) against FL systems caused by malicious participants, their primary aim is to decrease model accuracy, while the potential of leveraging poisonous model updates to exacerbate model unfairness remains unexplored. In this paper, we propose a new type of model poisoning attack, EAB-FL, with a focus on exacerbating group unfairness while maintaining a good level of model utility. Extensive experiments on three datasets demonstrate the effectiveness and efficiency of our attack, even with state-of-the-art fairness optimization algorithms and secure aggregation rules employed. We hope this work will help the community fully understand the attack surfaces of current FL systems and facilitate corresponding mitigation to improve their resilience.","doi":"10.24963/ijcai.2024/51","url":"https://ijcai.org/proceedings/2024/51","pdf":"https://ijcai.org/proceedings/2024/0051.pdf","markdown":"https://ijcai.org/proceedings/2024/51.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/51","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI Ethics, Trust, Fairness","track":"Main Track","number":51,"pages":{"first":458,"last":466},"keywords":["AI Ethics, Trust, Fairness: ETF: Fairness and diversity","AI Ethics, Trust, Fairness: ETF: Bias","Machine Learning: ML: Adversarial machine learning"],"videos":[],"references":[{"position":1,"raw":"[Abay et al., 2020] Annie Abay, Yi Zhou, Nathalie Bara- caldo, Shashank Rajamoni, Ebube Chuba, and Heiko Lud- wig. Mitigating bias in federated learning. arXiv preprint arXiv:2012.02447, 2020.","title":"Mitigating bias in federated learning","authors":"Annie Abay, Yi Zhou, Nathalie Baracaldo, Shashank Rajamoni, Ebube Chuba, Heiko Ludwig","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bach et al., 2015] Sebastian Bach, Alexander Binder, Gré- goire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS one, 10(7):e0130140, 2015.","title":"On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation","authors":"Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, Wojciech Samek","year":"2015","venue":"PloS one","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bagdasaryan et al., 2020] Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. How to backdoor federated learning. In International con- ference on artificial intelligence and statistics, pages 2938- 2948. PMLR, 2020.","title":"How to backdoor federated learning","authors":"Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, Vitaly Shmatikov","year":"2020","venue":"International conference on artificial intelligence and statistics","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bhagoji et al., 2019] Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo. Analyzing federated learning through an adversarial lens. In Interna- tional Conference on Machine Learning, pages 634-643.","title":"Analyzing federated learning through an adversarial lens","authors":"Nitin Arjun, Supriyo Bhagoji, Prateek Chakraborty, Seraphin Mittal, Calo","year":null,"venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"PMLR, 2019.","title":null,"authors":"Pmlr","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cao and Gong, 2022] Xiaoyu Cao and Neil Zhenqiang Gong. Mpaf: Model poisoning attacks to federated learning based on fake clients. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pages 3396-3404, 2022.","title":"Mpaf: Model poisoning attacks to federated learning based on fake clients","authors":"Gong ; ] Xiaoyu Cao, Neil Cao, Gong Zhenqiang","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chhabra et al., 2023] Anshuman Chhabra, Peizhao Li, Pras- ant Mohapatra, and Hongfu Liu. Robust fair clustering: A novel fairness attack and defense framework. International Conference on Learning Representations (ICLR), 2023.","title":"Robust fair clustering: A novel fairness attack and defense framework","authors":"Anshuman Chhabra, Peizhao Li, Prasant Mohapatra, Hongfu Liu","year":"2023","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Chu et al., 2021] Lingyang Chu, Lanjun Wang, Yanjie Dong, Jian Pei, Zirui Zhou, and Yong Zhang. Fedfair: Training fair models in cross-silo federated learning. arXiv preprint arXiv:2109.05662, 2021.","title":"Fedfair: Training fair models in cross-silo federated learning","authors":"Chu","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Cpu-Monkey, 2024a] Cpu-Monkey. Apple A17 Pro (6 GPU Cores) Benchmark, Test and specs -cpu-monkey.com. https://www.cpu-monkey.com/en/igpu-apple_a17_pro_6_ gpu_cores, 2024. [Accessed 17-01-2024].","title":"Cpu-Monkey. Apple A17 Pro (6 GPU Cores) Benchmark, Test and specs -cpu-monkey","authors":null,"year":"2024","venue":null,"doi":null,"url":"https://www.cpu-monkey.com/en/igpu-apple_a17_pro_6_gpu_cores,2024","internal_url":null},{"position":10,"raw":"[Cpu-Monkey, 2024b] Cpu-Monkey. Intel Core i7-13700 Benchmark, Test and specs -cpu-monkey.com. https:// www.cpu-monkey.com/en/cpu-intel_core_i7_13700, 2024. [Accessed 17-01-2024].","title":"Cpu-Monkey. Intel Core i7-13700 Benchmark, Test and specs -cpu-monkey","authors":null,"year":"2024","venue":null,"doi":null,"url":"https://www.cpu-monkey.com/en/cpu-intel_core_i7_13700,2024","internal_url":null},{"position":11,"raw":"[Du et al., 2021] Wei Du, Depeng Xu, Xintao Wu, and Hang- hang Tong. Fairness-aware agnostic federated learning. In Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), pages 181-189. SIAM, 2021.","title":"Fairness-aware agnostic federated learning","authors":"Wei Du, Depeng Xu, Xintao Wu, Hanghang Tong","year":"2021","venue":"Proceedings of the 2021 SIAM International Conference on Data Mining (SDM)","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Dua and Graff, 2017] Dheeru Dua and Casey Graff. Uci machine learning repository. https://archive.ics.uci.edu/ ml/datasets/adult, 2017. [Online; accessed 17-December- 2021].","title":"Dheeru Dua and Casey Graff. Uci machine learning repository","authors":"Graff Dua","year":"2017","venue":null,"doi":null,"url":"https://archive.ics.uci.edu/ml/datasets/adult,2017","internal_url":null},{"position":13,"raw":"[Dwork et al., 2012] Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. Fairness through awareness. In Proceedings of the 3rd innovations in theoretical computer science conference, pages 214-226, 2012.","title":"Fairness through awareness","authors":"Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Richard Zemel","year":"2012","venue":"Proceedings of the 3rd innovations in theoretical computer science conference","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Ezzeldin et al., 2023] Yahya H Ezzeldin, Shen Yan, Chaoyang He, Emilio Ferrara, and A Salman Avestimehr. Fairfed: Enabling group fairness in federated learning. Pro- ceedings of the AAAI Conference on Artificial Intelligence, 37(6):7494-7502, 2023.","title":"Fairfed: Enabling group fairness in federated learning","authors":"Shen Yahya H Ezzeldin, Chaoyang Yan, Emilio He, Salman Ferrara, Avestimehr","year":"2023","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Hardt et al., 2016] Moritz Hardt, Eric Price, and Nati Srebro. Equality of opportunity in supervised learning. Advances in neural information processing systems, 29, 2016.","title":"Equality of opportunity in supervised learning","authors":"Moritz Hardt, Eric Price, Nati Srebro","year":"2016","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Harper and Konstan, 2015] F Maxwell Harper and Joseph A Konstan. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), 5(4):1-19, 2015.","title":"The movielens datasets: History and context. Acm transactions on interactive intelligent systems","authors":"Maxwell Harper, Joseph A Konstan","year":"2015","venue":"tiis)","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Hossain et al., 2021] Md Tamjid Hossain, Shafkat Islam, Shahriar Badsha, and Haoting Shen. Desmp: Differen- tial privacy-exploited stealthy model poisoning attacks in federated learning. In 2021 17th International Conference on Mobility, Sensing and Networking (MSN), pages 167- 174. IEEE, 2021.","title":"Desmp: Differential privacy-exploited stealthy model poisoning attacks in federated learning","authors":"Md Tamjid Hossain, Shafkat Islam, Shahriar Badsha, Haoting Shen","year":"2021","venue":"17th International Conference on Mobility, Sensing and Networking (MSN)","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Jacot et al., 2018] Arthur Jacot, Franck Gabriel, and Clé- ment Hongler. Neural tangent kernel: Convergence and generalization in neural networks. Advances in neural in- formation processing systems, 31, 2018.","title":"Neural tangent kernel: Convergence and generalization in neural networks","authors":"Jacot","year":"2018","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Konečný et al., 2016] Jakub Konečný, H. Brendan McMa- han, Felix X. Yu, Peter Richtarik, Ananda Theertha Suresh, and Dave Bacon. Federated learning: Strategies for im- proving communication efficiency. In NIPS Workshop on Private Multi-Party Machine Learning, 2016.","title":"Federated learning: Strategies for improving communication efficiency","authors":"H Brendan Jakub Konečný, Felix X Mcmahan, Peter Yu, Ananda Richtarik, Dave Theertha Suresh, Bacon","year":"2016","venue":"NIPS Workshop on Private Multi-Party Machine Learning","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Li et al., 2019a] Liping Li, Wei Xu, Tianyi Chen, Geor- gios B Giannakis, and Qing Ling. Rsa: Byzantine-robust stochastic aggregation methods for distributed learning from heterogeneous datasets. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 1544-1551, 2019.","title":"Rsa: Byzantine-robust stochastic aggregation methods for distributed learning from heterogeneous datasets","authors":"Li","year":"2019","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Li et al., 2019b] Xilai Li, Yingbo Zhou, Tianfu Wu, Richard Socher, and Caiming Xiong. Learn to grow: A continual structure learning framework for overcoming catastrophic forgetting. In International Conference on Machine Learn- ing, pages 3925-3934. PMLR, 2019.","title":"Learn to grow: A continual structure learning framework for overcoming catastrophic forgetting","authors":"Li","year":"2019","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Li et al., 2020] Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith. Fair resource allocation in federated learning. International Conference on Learning Represen- tations (ICLR), 2020.","title":"Fair resource allocation in federated learning","authors":"Tian Li, Maziar Sanjabi, Ahmad Beirami, Virginia Smith","year":"2020","venue":"International Conference on Learning Representations (ICLR)","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Li et al., 2021a] Tian Li, Shengyuan Hu, Ahmad Beirami, and Virginia Smith. Ditto: Fair and robust federated learn- ing through personalization. In International Conference on Machine Learning, pages 6357-6368. PMLR, 2021.","title":"Ditto: Fair and robust federated learning through personalization","authors":"Li","year":"2021","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Li et al., 2021b] Xingyu Li, Zhe Qu, Shangqing Zhao, Bo Tang, Zhuo Lu, and Yao Liu. Lomar: A local de- fense against poisoning attack on federated learning. IEEE Transactions on Dependable and Secure Computing, 2021.","title":"Lomar: A local defense against poisoning attack on federated learning","authors":"Li","year":"2021","venue":"IEEE Transactions on Dependable and Secure Computing","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Li et al., 2022] Henger Li, Xiaolin Sun, and Zizhan Zheng. Learning to attack federated learning: A model-based rein- forcement learning attack framework. Advances in Neural Information Processing Systems, 35:35007-35020, 2022.","title":"Learning to attack federated learning: A model-based reinforcement learning attack framework","authors":"Henger Li, Xiaolin Sun, Zizhan Zheng","year":"2022","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Liu et al., 2018] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. Large-scale celebfaces attributes (celeba) dataset. Retrieved August, 15(2018):11, 2018.","title":"Large-scale celebfaces attributes (celeba) dataset","authors":"Ziwei Liu, Ping Luo, Xiaogang Wang, Xiaoou Tang","year":"2018","venue":"Retrieved","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Liu et al., 2023] Tao Liu, Zhi Wang, Hui He, Wei Shi, Lian- gliang Lin, Ran An, and Chenhao Li. Efficient and secure federated learning for financial applications. Applied Sci- ences, 13(10):5877, 2023.","title":"Efficient and secure federated learning for financial applications","authors":"Tao Liu, Zhi Wang, Hui He, Wei Shi, Liangliang Lin, Ran An, Chenhao Li","year":"2023","venue":"Applied Sciences","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Lyu et al., 2020] Lingjuan Lyu, Xinyi Xu, Qian Wang, and Han Yu. Collaborative fairness in federated learning. In Federated Learning, pages 189-204. Springer, 2020.","title":"Collaborative fairness in federated learning","authors":"Lyu","year":"2020","venue":"Federated Learning","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[McMahan et al., 2017] Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pages 1273-1282. PMLR, 2017.","title":"Communication-efficient learning of deep networks from decentralized data","authors":"Brendan Mcmahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Aguera Y Arcas","year":"2017","venue":"Artificial intelligence and statistics","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Mehrabi et al., 2021] Ninareh Mehrabi, Muhammad Naveed, Fred Morstatter, and Aram Galstyan. Ex- acerbating algorithmic bias through fairness attacks. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 8930-8938, 2021.","title":"Exacerbating algorithmic bias through fairness attacks","authors":"Mehrabi","year":"2021","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Mohri et al., 2019] Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh. Agnostic federated learning. In International Conference on Machine Learning, pages 4615-4625. PMLR, 2019.","title":"Agnostic federated learning","authors":"Mehryar Mohri, Gary Sivek, Ananda Theertha, Suresh","year":"2019","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Nvidia, 2022] Nvidia. NVIDIA A100 GPUs Power the Mod- ern Data Center -nvidia.com. https://www.nvidia.com/ en-us/data-center/a100/, 2022. [Accessed 17-01-2024].","title":"Nvidia. NVIDIA A100 GPUs Power the Modern Data Center -nvidia","authors":"Nvidia","year":"2022","venue":null,"doi":null,"url":"https://www.nvidia.com/en-us/data-center/a100/,2022","internal_url":null},{"position":33,"raw":"[Panda et al., 2022] Ashwinee Panda, Saeed Mahloujifar, Ar- jun Nitin Bhagoji, Supriyo Chakraborty, and Prateek Mittal. Sparsefed: Mitigating model poisoning attacks in federated learning with sparsification. In International Conference on Artificial Intelligence and Statistics, pages 7587-7624.","title":"Sparsefed: Mitigating model poisoning attacks in federated learning with sparsification","authors":"Panda","year":"2022","venue":"International Conference on Artificial Intelligence and Statistics","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"PMLR, 2022.","title":null,"authors":"Pmlr","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Pillutla et al., 2022] Krishna Pillutla, Sham M Kakade, and Zaid Harchaoui. Robust aggregation for federated learning. IEEE Transactions on Signal Processing, 70:1142-1154, 2022.","title":"Robust aggregation for federated learning","authors":"Krishna Pillutla, Zaid Sham M Kakade, Harchaoui","year":"2022","venue":"IEEE Transactions on Signal Processing","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Rauniyar et al., 2023] Ashish Rauniyar, Desta Haileselassie Hagos, Debesh Jha, Jan Erik Håkegård, Ulas Bagci, Danda B Rawat, and Vladimir Vlassov. Federated learn- ing for medical applications: A taxonomy, current trends, challenges, and future research directions. IEEE Internet of Things Journal, 2023.","title":"Federated learning for medical applications: A taxonomy, current trends, challenges, and future research directions","authors":"Ashish Rauniyar, Desta Haileselassie Hagos, Debesh Jha, Jan Erik Håkegård, Ulas Bagci, Vladimir Danda B Rawat, Vlassov","year":"2023","venue":"IEEE Internet of Things Journal","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Rodríguez-Gálvez et al., 2021] Borja Rodríguez-Gálvez, Filip Granqvist, Rogier van Dalen, and Matt Seigel. Enforcing fairness in private federated learning via the modified method of differential multipliers. In NeurIPS Workshop, 2021.","title":"Enforcing fairness in private federated learning via the modified method of differential multipliers","authors":"Borja Rodríguez-Gálvez, Filip Granqvist, Rogier Van Dalen, Matt Seigel","year":"2021","venue":"NeurIPS Workshop","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Shejwalkar and Houmansadr, 2021] Virat Shejwalkar and Amir Houmansadr. Manipulating the byzantine: Optimiz- ing model poisoning attacks and defenses for federated learning. In NDSS, 2021.","title":"Virat Shejwalkar and Amir Houmansadr. Manipulating the byzantine: Optimizing model poisoning attacks and defenses for federated learning","authors":"Houmansadr Shejwalkar","year":"2021","venue":"NDSS","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Shejwalkar et al., 2022] Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, and Daniel Ramage. Back to the drawing board: A critical evaluation of poisoning attacks on production federated learning. In 2022 IEEE Symposium on Security and Privacy (SP), pages 1354-1371. IEEE, 2022.","title":"Back to the drawing board: A critical evaluation of poisoning attacks on production federated learning","authors":"Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, Daniel Ramage","year":"2022","venue":"IEEE Symposium on Security and Privacy (SP)","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Solans et al., 2021] David Solans, Battista Biggio, and Car- los Castillo. Poisoning attacks on algorithmic fairness. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 162-177. Springer, 2021.","title":"Poisoning attacks on algorithmic fairness","authors":"David Solans, Battista Biggio, Carlos Castillo","year":"2021","venue":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Wang et al., 2022] Jialu Wang, Xin Eric Wang, and Yang Liu. Understanding instance-level impact of fairness con- straints. In International Conference on Machine Learning, pages 23114-23130. PMLR, 2022.","title":"Understanding instance-level impact of fairness constraints","authors":"Wang","year":"2022","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Xie et al., 2020] Chulin Xie, Keli Huang, Pin Yu Chen, and Bo Li. Dba: Distributed backdoor attacks against federated learning. In 8th International Conference on Learning Representations, ICLR 2020, 2020.","title":"Dba: Distributed backdoor attacks against federated learning","authors":"Chulin Xie, Keli Huang, Pin Yu Chen, Bo Li","year":"2020","venue":"8th International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Yin et al., 2018] Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett. Byzantine-robust distributed learning: Towards optimal statistical rates. In Interna- tional Conference on Machine Learning, pages 5650-5659.","title":"Byzantine-robust distributed learning: Towards optimal statistical rates","authors":"Dong Yin, Yudong Chen, Ramchandran Kannan, Peter Bartlett","year":null,"venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"PMLR, 2018.","title":null,"authors":"Pmlr","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Yue et al., 2023] Xubo Yue, Maher Nouiehed, and Raed Al Kontar. Gifair-fl: A framework for group and indi- vidual fairness in federated learning. INFORMS Journal on Data Science, 2(1):10-23, 2023.","title":"Gifair-fl: A framework for group and individual fairness in federated learning","authors":"Yue","year":"2023","venue":"INFORMS Journal on Data Science","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Zeng et al., 2022] Yuchen Zeng, Hongxu Chen, and Kang- wook Lee. Improving fairness via federated learning. arXiv preprint arXiv:2110.15545, 2022.","title":"Improving fairness via federated learning","authors":"Yuchen Zeng, Hongxu Chen, Kangwook Lee","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Zhang et al., 2017] Zhifei Zhang, Yang Song, and Hairong Qi. Age progression/regression by conditional adversarial autoencoder. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017.","title":"Age progression/regression by conditional adversarial autoencoder","authors":"Zhang","year":"2017","venue":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Zhang et al., 2020] Daniel Yue Zhang, Ziyi Kou, and Dong Wang. Fairfl: A fair federated learning approach to reducing demographic bias in privacy-sensitive classification models. In 2020 IEEE International Conference on Big Data (Big Data), pages 1051-1060. IEEE, 2020.","title":"Fairfl: A fair federated learning approach to reducing demographic bias in privacy-sensitive classification models","authors":"Ziyi Daniel Yue Zhang, Dong Kou, Wang","year":"2020","venue":"IEEE International Conference on Big Data (Big Data)","doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Zhang et al., 2022] Zaixi Zhang, Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong. Fldetector: Defending federated learning against model poisoning attacks via detecting ma- licious clients. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 2545-2555, 2022.","title":"Fldetector: Defending federated learning against model poisoning attacks via detecting malicious clients","authors":"Zhang","year":"2022","venue":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Zhong et al., 2022] Zhengyi Zhong, Weidong Bao, Ji Wang, Xiaomin Zhu, and Xiongtao Zhang. Flee: A hierarchical federated learning framework for distributed deep neural network over cloud, edge, and end device. ACM Transac- tions on Intelligent Systems and Technology (TIST), 13(5):1- 24, 2022.","title":"Flee: A hierarchical federated learning framework for distributed deep neural network over cloud, edge, and end device","authors":"Zhengyi Zhong, Weidong Bao, Ji Wang, Xiaomin Zhu, Xiongtao Zhang","year":"2022","venue":"ACM Transactions on Intelligent Systems and Technology (TIST)","doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Zhou et al., 2021] Xingchen Zhou, Ming Xu, Yiming Wu, and Ning Zheng. Deep model poisoning attack on federated learning. Future Internet, 13(3):73, 2021.","title":"Deep model poisoning attack on federated learning","authors":"Xingchen Zhou, Ming Xu, Yiming Wu, Ning Zheng","year":"2021","venue":"Future Internet","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}