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add FL fairness paper-list (#407)
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Osier-Yi authored Oct 24, 2022
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Expand Up @@ -270,7 +270,7 @@ As a comprehensive FL platform, FederatedScope provides the fundamental implemen

More supports are coming soon! We have prepared a [tutorial](https://federatedscope.io/) to provide more details about how to utilize FederatedScope to enjoy your journey of Federated Learning!

Materials of related topics are constantly being updated, please refer to [FL-Recommendation](https://github.com/alibaba/FederatedScope/tree/master/materials/paper_list/FL-Recommendation), [Federated-HPO](https://github.com/alibaba/FederatedScope/tree/master/materials/paper_list/Federated_HPO), [Personalized FL](https://github.com/alibaba/FederatedScope/tree/master/materials/paper_list/Personalized_FL), [Federated Graph Learning](https://github.com/alibaba/FederatedScope/tree/master/materials/paper_list/Federated_Graph_Learning), [FL-NLP](https://github.com/alibaba/FederatedScope/tree/master/materials/paper_list/FL-NLP), [FL-Attacker](https://github.com/alibaba/FederatedScope/tree/master/materials/paper_list/FL-Attacker), [FL-Incentive-Mechanism](https://github.com/alibaba/FederatedScope/tree/master/materials/paper_list/FL-Incentive) and so on.
Materials of related topics are constantly being updated, please refer to [FL-Recommendation](https://github.com/alibaba/FederatedScope/tree/master/materials/paper_list/FL-Recommendation), [Federated-HPO](https://github.com/alibaba/FederatedScope/tree/master/materials/paper_list/Federated_HPO), [Personalized FL](https://github.com/alibaba/FederatedScope/tree/master/materials/paper_list/Personalized_FL), [Federated Graph Learning](https://github.com/alibaba/FederatedScope/tree/master/materials/paper_list/Federated_Graph_Learning), [FL-NLP](https://github.com/alibaba/FederatedScope/tree/master/materials/paper_list/FL-NLP), [FL-Attacker](https://github.com/alibaba/FederatedScope/tree/master/materials/paper_list/FL-Attacker), [FL-Incentive-Mechanism](https://github.com/alibaba/FederatedScope/tree/master/materials/paper_list/FL-Incentive), [FL-Fairness](https://github.com/alibaba/FederatedScope/tree/master/materials/paper_list/FL-Fiarness) and so on.

## Documentation

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# Fairness in Federated Learning


## Fairness - Demographic Disparity
### 2022
| Title | Venue | Link
| ------------------------------------------------------------ | ---------- |---------------------------------------------
| Privfairfl: Privacy-preserving group fairness in federated learning | arxiv | [pdf](https://arxiv.org/pdf/2205.11584v1.pdf)
| Fair federated learning for heterogeneous data | IKDD CODS & COMAD | [pdf](https://dl.acm.org/doi/fullHtml/10.1145/3493700.3493750)


### 2021
| Title | Venue | Link
| ------------------------------------------------------------ | ---------- |---------------------------------------------
| Addressing algorithmic disparity and performance inconsistency in federated learning| NeurIPS Workshop| [pdf](https://arxiv.org/pdf/2108.08435.pdf)
|Enforcing fairness in private federated learning via the modified method of differential multipliers| NeurIPS Workshop| [pdf](https://arxiv.org/abs/2109.08604)
| Fairness-aware agnostic federated learning | SDM|[pdf](https://arxiv.org/pdf/2010.05057.pdf)
| Federated adversarial debiasing for fair and transferable representations| KDD| [pdf](https://dl.acm.org/doi/pdf/10.1145/3447548.3467281)


### 2019
| Title | Venue | Link
| ------------------------------------------------------------ | ---------- |---------------------------------------------
|Agnostic federated learning | ICML| [pdf](http://proceedings.mlr.press/v97/mohri19a/mohri19a.pdf)


## Fairness - Client performance parity

### Survey
| Title | Venue | Link
| ------------------------------------------------------------ | ---------- |---------------------------------------------
|Non-iid data and continual learning processes in federated learning: A long road ahead| Information Fution| [pdf](https://arxiv.org/pdf/2111.13394.pdf)
|Federated learning on non-iid data silos: An experimental study | ICDE|[pdf](https://arxiv.org/pdf/2102.02079.pdf)
|Federated learning on non-iid data: A survey | Neurocomputing | [pdf](https://arxiv.org/pdf/2106.06843.pdf)


### 2022
| Title | Venue | Link
| ------------------------------------------------------------ | ---------- |---------------------------------------------
|Fedmgda+: Federated learning meets multi-objective optimization | IEEE Transactions on Network Science and Engineering| [pdf](https://arxiv.org/pdf/2006.11489.pdf)


### 2021
| Title | Venue | Link
| ------------------------------------------------------------ | ---------- |---------------------------------------------
|Ditto: Fair and robust federated learning through personalization| ICML | [pdf](http://proceedings.mlr.press/v139/li21h/li21h.pdf)


### 2020
| Title | Venue | Link
| ------------------------------------------------------------ | ---------- |---------------------------------------------
|Federated optimization in heterogeneous networks| ICML| [pdf](https://arxiv.org/pdf/1812.06127.pdf)
|Fair resource allocation in federated learning | ICLR | [pdf](https://arxiv.org/pdf/1905.10497.pdf)
| Scaffold: Stochastic controlled averaging for federated learning| ICML | [pdf](https://arxiv.org/pdf/1910.06378.pdf)


## Fairness - Collaborative fairness

### Survey
| Title | Venue | Link
| ------------------------------------------------------------ | ---------- |---------------------------------------------
|A comprehensive survey of incentive mechanism for federated learning| arXiv | [pdf](https://arxiv.org/pdf/2106.15406.pdf)


### 2022
| Title | Venue | Link
| ------------------------------------------------------------ | ---------- |---------------------------------------------
|Collaboration equilibrium in federated learning| KDD | [pdf](https://arxiv.org/pdf/2108.07926.pdf)
| Fedfaim: A model performance-based fair incentive mechanism for federated learning| IEEE Transactionson Big Data| [pdf](https://ieeexplore.ieee.org/document/9797864)



### 2021
| Title | Venue | Link
| ------------------------------------------------------------ | ---------- |---------------------------------------------
|Incentive mechanism for horizontal federated learning based on reputation and reverse auction | WWW | [pdf](https://dl.acm.org/doi/10.1145/3442381.3449888)
| One for one, or all for all: Equilibria and optimality of collaboration in federated learning | ICML | [pdf](https://arxiv.org/pdf/2103.03228.pdf)


### 2020
| Title | Venue | Link
| ------------------------------------------------------------ | ---------- |---------------------------------------------
|A fairness-aware incentive scheme for federated learning| AIES | [pdf](https://dl.acm.org/doi/10.1145/3375627.3375840)
| Collaborative fairness in federated learning | Federated Learning | [pdf](https://arxiv.org/pdf/2008.12161.pdf)
| Towards fair and privacy-preserving federated deep models | IEEE TPDS | [pdf](https://arxiv.org/pdf/1906.01167.pdf)

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