GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning

ZELEI LIU, School of Computer Science and Engineering, Nanyang Technological University, Singapore YUANYUAN CHEN, School of Computer Science and Engineering, Nanyang Technological University, Singapore HAN YU, School of Computer Science and Engineering, Nanyang Technological University, Singapore YANG LIU, Institute for AI Industry Research, Tsinghua University, China LIZHEN CUI, School of Software, Shandong University, China

[1]  Chunyan Miao,et al.  A Survey of Trust and Reputation Management Systems in Wireless Communications , 2010, Proceedings of the IEEE.

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

[3]  Ximeng Liu,et al.  When Crowdsensing Meets Federated Learning: Privacy-Preserving Mobile Crowdsensing System , 2021, ArXiv.

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

[5]  Yang Liu,et al.  A Sustainable Incentive Scheme for Federated Learning , 2020, IEEE Intelligent Systems.

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

[7]  Han Yu,et al.  FOCUS: Dealing with Label Quality Disparity in Federated Learning , 2020, Federated Learning.

[8]  Agustí Verde Parera,et al.  General data protection regulation , 2018 .

[9]  Daniel Gómez,et al.  Polynomial calculation of the Shapley value based on sampling , 2009, Comput. Oper. Res..

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

[11]  John Augustine,et al.  Dynamics of Profit-Sharing Games , 2010, IJCAI.

[12]  Shengli Xie,et al.  Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory , 2019, IEEE Internet of Things Journal.

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

[14]  Vincent W. S. Wong,et al.  An Incentive Mechanism for Cross-Silo Federated Learning: A Public Goods Perspective , 2021, IEEE INFOCOM 2021 - IEEE Conference on Computer Communications.

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

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

[17]  Boi Faltings,et al.  Rewarding High-Quality Data via Influence Functions , 2019, ArXiv.

[18]  Talal Rahwan,et al.  Bounding the Estimation Error of Sampling-based Shapley Value Approximation With/Without Stratifying , 2013, ArXiv.

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

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

[21]  Zhixuan Fang,et al.  Incentive Mechanism Design for Federated Learning with Multi-Dimensional Private Information , 2020, WiOpt.

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

[23]  L. Shapley.  A Value for n-person Games , 1988 .

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

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

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

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

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

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

[30]  Herbert Hamers,et al.  A new approximation method for the Shapley value applied to the WTC 9/11 terrorist attack , 2018, Social Network Analysis and Mining.

[31]  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).

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