diff --git a/README.md b/README.md index c7075e729..273b8e694 100644 --- a/README.md +++ b/README.md @@ -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 diff --git a/materials/paper_list/FL-Fairness/README.md b/materials/paper_list/FL-Fairness/README.md new file mode 100644 index 000000000..1f0bd7167 --- /dev/null +++ b/materials/paper_list/FL-Fairness/README.md @@ -0,0 +1,86 @@ +# 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) +