extended-abstract

Fair Federated Learning for Heterogeneous Data

Authors:

Samhita Kanaparthy,

Manisha Padala,

Sankarshan Damle,

Sujit GujarAuthors Info & Claims

CODS-COMAD '22: Proceedings of the 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD)

Pages 298 - 299

https://doi.org/10.1145/3493700.3493750

Published: 08 January 2022 Publication History

Get Access

Abstract

We consider the problem of achieving fair classification in Federated Learning (FL) under data heterogeneity. Most of the approaches proposed for fair classification require diverse data that represent the different demographic groups involved. In contrast, it is common for each client to own data that represents only a single demographic group. Hence the existing approaches cannot be adopted for fair classification models at the client level. To resolve this challenge, we propose several aggregation techniques. We empirically validate these techniques by comparing the resulting fairness and accuracy on CelebA and UTK datasets.

References

[1]

Alexandra Chouldechova. 2017. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data 5 2(2017), 153–163.

Google Scholar

[2]

Muhammad Habib ur Rehman, Ahmed Mukhtar Dirir, Khaled Salah, and Davor Svetinovic. 2020. FairFed: Cross-Device Fair Federated Learning. In 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). 1–7.

Google Scholar

[3]

Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage. 2018. Federated Learning for Mobile Keyboard Prediction. ArXiv abs/1811.03604(2018).

Google Scholar

[4]

Samhita Kanaparthy, Padala Manisha, Sankarshan Damle, and Sujit Gujar. 2021. Fair Federated Learning for Heterogeneous Face Data. CoRR abs/2109.02351(2021). arXiv:2109.02351https://arxiv.org/abs/2109.02351

Google Scholar

[5]

Li Li, Yuxi Fan, Mike Tse, and Kuo-Yi Lin. 2020. A review of applications in federated learning. Computers & Industrial Engineering 149 (2020), 106854.

Crossref

Google Scholar

[6]

Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. 2015. Deep learning face attributes in the wild. In Proceedings of the IEEE international conference on computer vision. 3730–3738.

Digital Library

Google Scholar

[7]

Vishnu Suresh Lokhande, Aditya Kumar Akash, Sathya N Ravi, and Vikas Singh. 2020. Fairalm: Augmented lagrangian method for training fair models with little regret. In European Conference on Computer Vision. Springer, 365–381.

Digital Library

Google Scholar

[8]

Padala Manisha, Sankarshan Damle, and Sujit Gujar. 2021. Federated Learning Meets Fairness and Differential Privacy. ArXiv abs/2108.09932(2021).

Google Scholar

[9]

Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics. PMLR, 1273–1282.

Google Scholar

[10]

Manisha Padala and Sujit Gujar. 2020. FNNC: Achieving Fairness through Neural Networks. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence,{IJCAI-20}.

Crossref

Google Scholar

[11]

Sahil Verma and Julia Rubin. 2018. Fairness definitions explained. In 2018 ieee/acm international workshop on software fairness (fairware). IEEE, 1–7.

Digital Library

Google Scholar

[12]

Omar Wahab, Azzam Mourad, Hadi Otrok, and Tarik Taleb. 2021. Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems. IEEE Communications Surveys & Tutorials 23 (02 2021). https://doi.org/10.1109/COMST.2021.3058573

Crossref

Google Scholar

[13]

Zhifei Zhang, Yang Song, and Hairong Qi. 2017. Age progression/regression by conditional adversarial autoencoder. In Proceedings of the IEEE conference on computer vision and pattern recognition. 5810–5818.

Crossref

Google Scholar

Cited By

View all

Chen HZhu TZhang TZhou WYu P(2023)Privacy and Fairness in Federated Learning: On the Perspective of TradeoffACM Computing Surveys10.1145/360601756:2(1-37)Online publication date: 15-Sep-2023
https://dl.acm.org/doi/10.1145/3606017
Onsu MKantarci BBoukerche A(2023)How to cope with malicious federated learning clientsComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2023.109938234:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.comnet.2023.109938
Gupta SJain SGhalme GKrishnan NHemachandra N(2023)Group and Individual Fairness in Clustering AlgorithmsEthics in Artificial Intelligence: Bias, Fairness and Beyond10.1007/978-981-99-7184-8_2(31-51)Online publication date: 30-Dec-2023
https://doi.org/10.1007/978-981-99-7184-8_2
Show More Cited By

Index Terms

Fair Federated Learning for Heterogeneous Data
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

Recommendations

Heterogeneity-aware fair federated learning
Abstract
The fairness of federated learning means that the global model cannot discriminate against any group. Due data heterogeneity, the update direction of some clients will hinder other clients, so the global model is difficult to treat ...
F3: Fair and Federated Face Attribute Classification with Heterogeneous Data
Advances in Knowledge Discovery and Data Mining
Abstract
Fairness across different demographic groups is an essential criterion for face-related tasks, Face Attribute Classification (FAC) being a prominent example. Simultaneously, federated Learning (FL) is gaining traction as a scalable paradigm for ...
Ensuring Fairness and Gradient Privacy in Personalized Heterogeneous Federated Learning
With the increasing tension between conflicting requirements of the availability of large amounts of data for effective machine learning-based analysis, and for ensuring their privacy, the paradigm of federated learning has emerged, a distributed machine ...

Comments

Information & Contributors

Information

Published In

CODS-COMAD '22: Proceedings of the 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD)

January 2022

357 pages

ISBN:9781450385824

DOI:10.1145/3493700

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 January 2022

Check for updates

Author Tags

Qualifiers

Extended-abstract
Research
Refereed limited

Conference

CODS-COMAD 2022

Sponsor:

SIGGRAPH

CODS-COMAD 2022: 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD)

January 8 - 10, 2022

Bangalore, India

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
415
Total Downloads

Downloads (Last 12 months)100
Downloads (Last 6 weeks)11

Reflects downloads up to 27 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Chen HZhu TZhang TZhou WYu P(2023)Privacy and Fairness in Federated Learning: On the Perspective of TradeoffACM Computing Surveys10.1145/360601756:2(1-37)Online publication date: 15-Sep-2023
https://dl.acm.org/doi/10.1145/3606017
Onsu MKantarci BBoukerche A(2023)How to cope with malicious federated learning clientsComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2023.109938234:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.comnet.2023.109938
Gupta SJain SGhalme GKrishnan NHemachandra N(2023)Group and Individual Fairness in Clustering AlgorithmsEthics in Artificial Intelligence: Bias, Fairness and Beyond10.1007/978-981-99-7184-8_2(31-51)Online publication date: 30-Dec-2023
https://doi.org/10.1007/978-981-99-7184-8_2
Huang YXu YKong LLi QCui L(2023)Towards Heterogeneous Federated LearningComputer Supported Cooperative Work and Social Computing10.1007/978-981-99-2356-4_31(390-404)Online publication date: 13-May-2023
https://doi.org/10.1007/978-981-99-2356-4_31

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Abstract

References

Cited By

Index Terms

Recommendations

Heterogeneity-aware fair federated learning

F3: Fair and Federated Face Attribute Classification with Heterogeneous Data

Ensuring Fairness and Gradient Privacy in Personalized Heterogeneous Federated Learning

Comments

Information

Published In

Sponsors

Publisher

Publication History

Check for updates

Author Tags

Qualifiers

Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Get Access

Login options

Full Access

View options

PDF

eReader

HTML Format

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations