A Survey of Fairness-Aware Federated Learning

Recent advances in Federated Learning (FL) have brought large-scale machine learning opportunities for massive distributed clients with performance and data privacy guarantees. However, most current works only focus on the interest of the central controller in FL, and ignore the interests of clients. This may result in unfairness which discourages clients from actively participating in the learning process and damages the sustainability of the whole FL system. Therefore, the topic of ensuring fairness in an FL is attracting a great deal of research interest. In recent years, diverse Fairness-Aware FL (FAFL) approaches have been proposed in an effort to achieve fairness in FL from different viewpoints. However, there is no comprehensive survey which helps readers gain insight into this interdisciplinary field. This paper aims to provide such a survey. By examining the fundamental and simplifying assumptions, as well as the notions of fairness adopted by existing literature in this field, we propose a taxonomy of FAFL approaches covering major steps in FL, including client selection, optimization, contribution evaluation and incentive distribution. In addition, we discuss the main metrics for experimentally evaluating the performance of FAFL approaches, and suggest some promising future research directions.

[1]  Yongxin Tong,et al.  Efficient and Fair Data Valuation for Horizontal Federated Learning , 2020, Federated Learning.

[2]  Rongfei Zeng,et al.  A Comprehensive Survey of Incentive Mechanism for Federated Learning , 2021, ArXiv.

[3]  Choong Seon Hong,et al.  A Crowdsourcing Framework for On-Device Federated Learning , 2020, IEEE Transactions on Wireless Communications.

[4]  Reza Shokri,et al.  On the Privacy Risks of Model Explanations , 2019, AIES.

[5]  Xiaowen Chu,et al.  FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC , 2020, ArXiv.

[6]  Kristina Lerman,et al.  A Survey on Bias and Fairness in Machine Learning , 2019, ACM Comput. Surv..

[7]  Anit Kumar Sahu,et al.  Federated Optimization in Heterogeneous Networks , 2018, MLSys.

[8]  Costas J. Spanos,et al.  Towards Efficient Data Valuation Based on the Shapley Value , 2019, AISTATS.

[9]  Jiong Jin,et al.  Towards Fair and Privacy-Preserving Federated Deep Models , 2019, IEEE Transactions on Parallel and Distributed Systems.

[10]  A. Young.  Contract Theory , 2011 .

[11]  Haris Vikalo,et al.  Communication-Efficient Federated Learning via Optimal Client Sampling , 2020, LatinX in AI at International Conference on Machine Learning 2020.

[12]  Qiang Yang,et al.  Towards Personalized Federated Learning , 2021, IEEE transactions on neural networks and learning systems.

[13]  Mohsen Guizani,et al.  Reliable Federated Learning for Mobile Networks , 2019, IEEE Wireless Communications.

[14]  Xia Hu,et al.  Fairness in Deep Learning: A Computational Perspective , 2019, IEEE Intelligent Systems.

[15]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[16]  Anuj Kumar,et al.  Active Federated Learning , 2019, ArXiv.

[17]  Takayuki Nishio,et al.  Estimation of Individual Device Contributions for Incentivizing Federated Learning , 2020, 2020 IEEE Globecom Workshops (GC Wkshps.

[18]  Siu-Ming Yiu,et al.  A Game-Theoretic Framework for Incentive Mechanism Design in Federated Learning , 2020, Federated Learning.

[19]  Ninghui Li,et al.  Privacy at Scale: Local Dierential Privacy in Practice , 2018 .

[20]  Anit Kumar Sahu,et al.  Federated Learning: Challenges, Methods, and Future Directions , 2019, IEEE Signal Processing Magazine.

[21]  Richard Nock,et al.  Advances and Open Problems in Federated Learning , 2021, Found. Trends Mach. Learn..

[22]  Ali Dehghantanha,et al.  A survey on security and privacy of federated learning , 2021, Future Gener. Comput. Syst..

[23]  Sreenivas Gollapudi,et al.  Profit Sharing and Efficiency in Utility Games , 2017, ESA.

[24]  Lingjuan Lyu,et al.  Collaborative Fairness in Federated Learning , 2020, Federated Learning.

[25]  Hao Wang,et al.  Optimizing Federated Learning on Non-IID Data with Reinforcement Learning , 2020, IEEE INFOCOM 2020 - IEEE Conference on Computer Communications.

[26]  J. Cruz,et al.  On the Stackelberg strategy in nonzero-sum games , 1973 .

[27]  Blaise Agüera y Arcas,et al.  Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.

[28]  Tianjian Chen,et al.  A Fairness-aware Incentive Scheme for Federated Learning , 2020, AIES.

[29]  Pengyuan Zhou,et al.  Loss Tolerant Federated Learning , 2021, ArXiv.

[30]  Choong Seon Hong,et al.  An Incentive Mechanism for Federated Learning in Wireless Cellular Networks: An Auction Approach , 2020, IEEE Transactions on Wireless Communications.

[31]  Siddharth Divi,et al.  New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning , 2021, ArXiv.

[32]  Sercan O. Arik,et al.  Data Valuation using Reinforcement Learning , 2019, ICML.

[33]  Qi Li,et al.  Enabling Execution Assurance of Federated Learning at Untrusted Participants , 2020, IEEE INFOCOM 2020 - IEEE Conference on Computer Communications.

[34]  Tian Li,et al.  Fair Resource Allocation in Federated Learning , 2019, ICLR.

[35]  Chunyan Miao,et al.  A Survey of Multi-Agent Trust Management Systems , 2013, IEEE Access.

[36]  M. Rabin.  Published by: American , 2022 .

[37]  João Gama,et al.  A survey on concept drift adaptation , 2014, ACM Comput. Surv..

[38]  A. Anonymous,et al.  Consumer Data Privacy in a Networked World: A Framework for Protecting Privacy and Promoting Innovation in the Global Digital Economy , 2013, J. Priv. Confidentiality.

[39]  Albert Y. Zomaya,et al.  An Efficiency-Boosting Client Selection Scheme for Federated Learning With Fairness Guarantee , 2020, IEEE Transactions on Parallel and Distributed Systems.

[40]  Jingwen Zhang,et al.  Incentive Mechanism for Horizontal Federated Learning Based on Reputation and Reverse Auction , 2021, WWW.

[41]  Yeon-Koo Che.  Design competition through multidimensional auctions , 1993 .

[42]  Bastin Tony Roy Savarimuthu,et al.  Norm creation, spreading and emergence: A survey of simulation models of norms in multi-agent systems , 2011, Multiagent Grid Syst..

[43]  Han Yu,et al.  A VCG-based Fair Incentive Mechanism for Federated Learning , 2020, ArXiv.

[44]  Ying-Chang Liang,et al.  Federated Learning in Mobile Edge Networks: A Comprehensive Survey , 2020, IEEE Communications Surveys & Tutorials.

[45]  Krishna P. Gummadi,et al.  Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment , 2016, WWW.

[46]  Takayuki Nishio,et al.  Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge , 2018, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[47]  Sung Kuk Shyn,et al.  FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning , 2021, ArXiv.

[48]  L. S. Shapley,et al.  17. A Value for n-Person Games , 1953 .

[49]  Yunus Sarikaya,et al.  Motivating Workers in Federated Learning: A Stackelberg Game Perspective , 2019, IEEE Networking Letters.

[50]  Michael J. Dinneen,et al.  A Hybrid Geometric Approach for Measuring Similarity Level Among Documents and Document Clustering , 2016, 2016 IEEE Second International Conference on Big Data Computing Service and Applications (BigDataService).

[51]  Hongbin Zhu,et al.  Federated Learning with Class Imbalance Reduction , 2020, 2021 29th European Signal Processing Conference (EUSIPCO).

[52]  Song Guo,et al.  A Survey of Incentive Mechanism Design for Federated Learning , 2021, IEEE Transactions on Emerging Topics in Computing.

[53]  James Y. Zou,et al.  Data Shapley: Equitable Valuation of Data for Machine Learning , 2019, ICML.

[54]  Yang Liu,et al.  Federated Learning , 2019, Synthesis Lectures on Artificial Intelligence and Machine Learning.

[55]  Ying-Chang Liang,et al.  Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach , 2019, 2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS).

[56]  Ying-Chang Liang,et al.  Joint Service Pricing and Cooperative Relay Communication for Federated Learning , 2018, 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).

[57]  Masahiro Morikura,et al.  Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data , 2019, ICC 2020 - 2020 IEEE International Conference on Communications (ICC).

[58]  P. Alam.  ‘Z’ , 2021, Composites Engineering: An A–Z Guide.

[59]  Hanghang Tong,et al.  Fairness-aware Agnostic Federated Learning , 2020, SDM.

[60]  Mehryar Mohri,et al.  Agnostic Federated Learning , 2019, ICML.

[61]  Walid Saad,et al.  Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism , 2019, IEEE Communications Magazine.

[62]  Miao Pan,et al.  Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach , 2020, IEEE Access.

[63]  Shuyue Wei,et al.  Profit Allocation for Federated Learning , 2019, 2019 IEEE International Conference on Big Data (Big Data).

[64]  Н. Грейда,et al.  17 , 2019, Magical Realism for Non-Believers.

[65]  Sebastian Caldas,et al.  Expanding the Reach of Federated Learning by Reducing Client Resource Requirements , 2018, ArXiv.

[66]  Cheng Wang,et al.  Federated Learning with Fair Averaging , 2021, IJCAI.

[67]  Han Yu,et al.  Threats to Federated Learning: A Survey , 2020, ArXiv.

[68]  Ernesto Damiani,et al.  TrustFed: A Framework for Fair and Trustworthy Cross-Device Federated Learning in IIoT , 2021, IEEE Transactions on Industrial Informatics.

[69]  Antonio Robles-Kelly,et al.  Hierarchically Fair Federated Learning , 2020, ArXiv.

[70]  Kin K. Leung,et al.  Energy-Efficient Resource Management for Federated Edge Learning With CPU-GPU Heterogeneous Computing , 2020, IEEE Transactions on Wireless Communications.

[71]  Xin Liu,et al.  Adaptive Federated Dropout: Improving Communication Efficiency and Generalization for Federated Learning , 2020, IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[72]  Dawn Song,et al.  A Principled Approach to Data Valuation for Federated Learning , 2020, Federated Learning.

[73]  Charu C. Aggarwal,et al.  On the Surprising Behavior of Distance Metrics in High Dimensional Spaces , 2001, ICDT.

[74]  Ziye Zhou,et al.  Measure Contribution of Participants in Federated Learning , 2019, 2019 IEEE International Conference on Big Data (Big Data).

[75]  Tianjian Chen,et al.  Federated Machine Learning: Concept and Applications , 2019 .

[76]  Yaoliang Yu,et al.  FedMGDA+: Federated Learning meets Multi-objective Optimization , 2020, ArXiv.

[77]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.