Towards Multi-Objective Statistically Fair Federated Learning Article Swipe
Ninareh Mehrabi
,
Cyprien de Lichy
,
John McKay
,
Cynthia Y. He
,
William W. Campbell
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2201.09917
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2201.09917
Federated Learning (FL) has emerged as a result of data ownership and privacy concerns to prevent data from being shared between multiple parties included in a training procedure. Although issues, such as privacy, have gained significant attention in this domain, not much attention has been given to satisfying statistical fairness measures in the FL setting. With this goal in mind, we conduct studies to show that FL is able to satisfy different fairness metrics under different data regimes consisting of different types of clients. More specifically, uncooperative or adversarial clients might contaminate the global FL model by injecting biased or poisoned models due to existing biases in their training datasets. Those biases might be a result of imbalanced training set (Zhang and Zhou 2019), historical biases (Mehrabi et al. 2021a), or poisoned data-points from data poisoning attacks against fairness (Mehrabi et al. 2021b; Solans, Biggio, and Castillo 2020). Thus, we propose a new FL framework that is able to satisfy multiple objectives including various statistical fairness metrics. Through experimentation, we then show the effectiveness of this method comparing it with various baselines, its ability in satisfying different objectives collectively and individually, and its ability in identifying uncooperative or adversarial clients and down-weighing their effect
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2201.09917
- https://arxiv.org/pdf/2201.09917
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221165996
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4221165996Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2201.09917Digital Object Identifier
- Title
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Towards Multi-Objective Statistically Fair Federated LearningWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-01-24Full publication date if available
- Authors
-
Ninareh Mehrabi, Cyprien de Lichy, John McKay, Cynthia Y. He, William W. CampbellList of authors in order
- Landing page
-
https://arxiv.org/abs/2201.09917Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2201.09917Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2201.09917Direct OA link when available
- Concepts
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Adversarial system, Computer science, Set (abstract data type), Federated learning, Zhàng, Training set, Domain (mathematical analysis), Data set, Machine learning, Artificial intelligence, Data science, Computer security, Mathematics, Political science, Law, Programming language, China, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2023: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.emerged | 4 |
| abstract_inverted_index.issues, | 29 |
| abstract_inverted_index.metrics | 73 |
| abstract_inverted_index.parties | 22 |
| abstract_inverted_index.prevent | 15 |
| abstract_inverted_index.privacy | 12 |
| abstract_inverted_index.propose | 150 |
| abstract_inverted_index.regimes | 77 |
| abstract_inverted_index.satisfy | 70, 159 |
| abstract_inverted_index.studies | 62 |
| abstract_inverted_index.various | 163, 180 |
| abstract_inverted_index.(Mehrabi | 126, 139 |
| abstract_inverted_index.Although | 28 |
| abstract_inverted_index.Castillo | 146 |
| abstract_inverted_index.Learning | 1 |
| abstract_inverted_index.clients. | 83 |
| abstract_inverted_index.concerns | 13 |
| abstract_inverted_index.existing | 104 |
| abstract_inverted_index.fairness | 49, 72, 138, 165 |
| abstract_inverted_index.included | 23 |
| abstract_inverted_index.measures | 50 |
| abstract_inverted_index.metrics. | 166 |
| abstract_inverted_index.multiple | 21, 160 |
| abstract_inverted_index.poisoned | 100, 131 |
| abstract_inverted_index.privacy, | 32 |
| abstract_inverted_index.setting. | 54 |
| abstract_inverted_index.training | 26, 108, 118 |
| abstract_inverted_index.Federated | 0 |
| abstract_inverted_index.attention | 36, 42 |
| abstract_inverted_index.comparing | 177 |
| abstract_inverted_index.datasets. | 109 |
| abstract_inverted_index.different | 71, 75, 80, 186 |
| abstract_inverted_index.framework | 154 |
| abstract_inverted_index.including | 162 |
| abstract_inverted_index.injecting | 97 |
| abstract_inverted_index.ownership | 10 |
| abstract_inverted_index.poisoning | 135 |
| abstract_inverted_index.baselines, | 181 |
| abstract_inverted_index.consisting | 78 |
| abstract_inverted_index.historical | 124 |
| abstract_inverted_index.imbalanced | 117 |
| abstract_inverted_index.objectives | 161, 187 |
| abstract_inverted_index.procedure. | 27 |
| abstract_inverted_index.satisfying | 47, 185 |
| abstract_inverted_index.adversarial | 88, 198 |
| abstract_inverted_index.contaminate | 91 |
| abstract_inverted_index.data-points | 132 |
| abstract_inverted_index.identifying | 195 |
| abstract_inverted_index.significant | 35 |
| abstract_inverted_index.statistical | 48, 164 |
| abstract_inverted_index.collectively | 188 |
| abstract_inverted_index.down-weighing | 201 |
| abstract_inverted_index.effectiveness | 173 |
| abstract_inverted_index.individually, | 190 |
| abstract_inverted_index.specifically, | 85 |
| abstract_inverted_index.uncooperative | 86, 196 |
| abstract_inverted_index.experimentation, | 168 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.5899999737739563 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |