Timezone: »
In collaborative machine learning(CML), multiple agents pool their resources(e.g., data) together for a common learning task. In realistic CML settings where the agents are self-interested and not altruistic, they may be unwilling to share data or model information without adequate rewards. Furthermore, as the data/model information shared by the agents may differ in quality, designing rewards which are fair to them is important so that they would not feel exploited nor discouraged from sharing. In this paper, we adopt federated learning as the CML paradigm, propose a novel cosine gradient Shapley value(CGSV) to fairly evaluate the expected marginal contribution of each agent’s uploaded model parameter update/gradient without needing an auxiliary validation dataset, and based on the CGSV, design a novel training-time gradient reward mechanism with a fairness guarantee by sparsifying the aggregated parameter update/gradient downloaded from the server as reward to each agent such that its resulting quality is commensurate to that of the agent’s uploaded parameter update/gradient. We empirically demonstrate the effectiveness of our fair gradient reward mechanism on multiple benchmark datasets in terms of fairness, predictive performance, and time overhead.
Author Information
Xinyi Xu (National University of Singapore)

I am a fourth year Ph.D. student (funded by A*STAR through the ACIS Scholarship) in the department of computer science at National University of Singapore where I study multi-agent machine learning systems.
Lingjuan Lyu (the University of Melbourne)
Xingjun Ma (Deakin University)
Chenglin Miao (University of Georgia)
Chuan Sheng Foo (Institute for Infocomm Research)
Bryan Kian Hsiang Low (National University of Singapore)
More from the Same Authors
-
2022 Poster: CalFAT: Calibrated Federated Adversarial Training with Label Skewness »
Chen Chen · Yuchen Liu · Xingjun Ma · Lingjuan Lyu -
2022 : MocoSFL: enabling cross-client collaborative self-supervised learning »
Jingtao Li · Lingjuan Lyu · Daisuke Iso · Chaitali Chakrabarti · Michael Spranger -
2023 Poster: Towards Personalized Federated Learning via Heterogeneous Model Reassembly »
Jiaqi Wang · Xingyi Yang · Suhan Cui · Liwei Che · Lingjuan Lyu · Dongkuan (DK) Xu · Fenglong Ma -
2023 Poster: Exploiting Correlated Auxiliary Feedback in Parameterized Bandits »
Arun Verma · Zhongxiang Dai · YAO SHU · Bryan Kian Hsiang Low -
2023 Poster: Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning »
Yue Tan · Chen Chen · Weiming Zhuang · Xin Dong · Lingjuan Lyu · Guodong Long -
2023 Poster: Where Did I Come From? Origin Attribution of AI-Generated Images »
Zhenting Wang · Chen Chen · Yi Zeng · Lingjuan Lyu · Shiqing Ma -
2023 Poster: Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception? »
Xiaoxiao Sun · Nidham Gazagnadou · Vivek Sharma · Lingjuan Lyu · Hongdong Li · Liang Zheng -
2023 Poster: Equitable Model Valuation with Black-box Access »
Xinyi Xu · Thanh Lam · Chuan Sheng Foo · Bryan Kian Hsiang Low -
2023 Poster: Quantum Bayesian Optimization »
Zhongxiang Dai · Gregory Kang Ruey Lau · Arun Verma · YAO SHU · Bryan Kian Hsiang Low · Patrick Jaillet -
2023 Poster: Batch Bayesian Optimization For Replicable Experimental Design »
Zhongxiang Dai · Quoc Phong Nguyen · Sebastian Tay · Daisuke Urano · Richalynn Leong · Bryan Kian Hsiang Low · Patrick Jaillet -
2023 Poster: Incentives in Private Collaborative Machine Learning »
Rachael Sim · Yehong Zhang · Nghia Hoang · Xinyi Xu · Bryan Kian Hsiang Low · Patrick Jaillet -
2023 Poster: UltraRE: Enhancing RecEraser for Recommendation Unlearning via Error Decomposition »
Yuyuan Li · Chaochao Chen · Yizhao Zhang · Weiming Liu · Lingjuan Lyu · Xiaolin Zheng · Dan Meng · Jun Wang -
2023 Poster: Bayesian Optimization with Cost-varying Variable Subsets »
Sebastian Tay · Chuan Sheng Foo · Daisuke Urano · Richalynn Leong · Bryan Kian Hsiang Low -
2022 Poster: Prompt Certified Machine Unlearning with Randomized Gradient Smoothing and Quantization »
Zijie Zhang · Yang Zhou · Xin Zhao · Tianshi Che · Lingjuan Lyu -
2022 Poster: CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks »
Xuanli He · Qiongkai Xu · Yi Zeng · Lingjuan Lyu · Fangzhao Wu · Jiwei Li · Ruoxi Jia -
2022 Poster: Trade-off between Payoff and Model Rewards in Shapley-Fair Collaborative Machine Learning »
Quoc Phong Nguyen · Bryan Kian Hsiang Low · Patrick Jaillet -
2022 Poster: Sample-Then-Optimize Batch Neural Thompson Sampling »
Zhongxiang Dai · YAO SHU · Bryan Kian Hsiang Low · Patrick Jaillet -
2022 Poster: FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning »
Tao Qi · Fangzhao Wu · Chuhan Wu · Lingjuan Lyu · Tong Xu · Hao Liao · Zhongliang Yang · Yongfeng Huang · Xing Xie -
2022 Poster: DENSE: Data-Free One-Shot Federated Learning »
Jie Zhang · Chen Chen · Bo Li · Lingjuan Lyu · Shuang Wu · Shouhong Ding · Chunhua Shen · Chao Wu -
2022 Poster: Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search »
YAO SHU · Zhongxiang Dai · Zhaoxuan Wu · Bryan Kian Hsiang Low -
2022 Poster: Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling »
Junyuan Hong · Lingjuan Lyu · Jiayu Zhou · Michael Spranger -
2021 Workshop: New Frontiers in Federated Learning: Privacy, Fairness, Robustness, Personalization and Data Ownership »
Nghia Hoang · Lam Nguyen · Pin-Yu Chen · Tsui-Wei Weng · Sara Magliacane · Bryan Kian Hsiang Low · Anoop Deoras -
2021 Poster: $\alpha$-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression »
JIABO HE · Sarah Erfani · Xingjun Ma · James Bailey · Ying Chi · Xian-Sheng Hua -
2021 Poster: Differentially Private Federated Bayesian Optimization with Distributed Exploration »
Zhongxiang Dai · Bryan Kian Hsiang Low · Patrick Jaillet -
2021 Poster: Anti-Backdoor Learning: Training Clean Models on Poisoned Data »
Yige Li · Xixiang Lyu · Nodens Koren · Lingjuan Lyu · Bo Li · Xingjun Ma -
2021 Poster: Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee »
Xiaofeng Fan · Yining Ma · Zhongxiang Dai · Wei Jing · Cheston Tan · Bryan Kian Hsiang Low -
2021 Poster: Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation »
Jinming Cui · Chaochao Chen · Lingjuan Lyu · Carl Yang · Wang Li -
2021 Poster: Optimizing Conditional Value-At-Risk of Black-Box Functions »
Quoc Phong Nguyen · Zhongxiang Dai · Bryan Kian Hsiang Low · Patrick Jaillet -
2021 Poster: Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks »
Hanxun Huang · Yisen Wang · Sarah Erfani · Quanquan Gu · James Bailey · Xingjun Ma -
2021 Poster: Validation Free and Replication Robust Volume-based Data Valuation »
Xinyi Xu · Zhaoxuan Wu · Chuan Sheng Foo · Bryan Kian Hsiang Low -
2020 Poster: Variational Bayesian Unlearning »
Quoc Phong Nguyen · Bryan Kian Hsiang Low · Patrick Jaillet -
2020 Poster: Federated Bayesian Optimization via Thompson Sampling »
Zhongxiang Dai · Bryan Kian Hsiang Low · Patrick Jaillet -
2020 Poster: Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization »
Sreejith Balakrishnan · Quoc Phong Nguyen · Bryan Kian Hsiang Low · Harold Soh -
2019 Poster: Implicit Posterior Variational Inference for Deep Gaussian Processes »
Haibin YU · Yizhou Chen · Bryan Kian Hsiang Low · Patrick Jaillet · Zhongxiang Dai -
2019 Spotlight: Implicit Posterior Variational Inference for Deep Gaussian Processes »
Haibin YU · Yizhou Chen · Bryan Kian Hsiang Low · Patrick Jaillet · Zhongxiang Dai -
2017 : Poster Session 2 »
Farhan Shafiq · Antonio Tomas Nevado Vilchez · Takato Yamada · Sakyasingha Dasgupta · Robin Geyer · Moin Nabi · Crefeda Rodrigues · Edoardo Manino · Alexantrou Serb · Miguel A. Carreira-Perpinan · Kar Wai Lim · Bryan Kian Hsiang Low · Rohit Pandey · Marie C White · Pavel Pidlypenskyi · Xue Wang · Christine Kaeser-Chen · Michael Zhu · Suyog Gupta · Sam Leroux -
2017 : Aligned AI Poster Session »
Amanda Askell · Rafal Muszynski · William Wang · Yaodong Yang · Quoc Nguyen · Bryan Kian Hsiang Low · Patrick Jaillet · Candice Schumann · Anqi Liu · Peter Eckersley · Angelina Wang · William Saunders -
2015 Poster: Inverse Reinforcement Learning with Locally Consistent Reward Functions »
Quoc Phong Nguyen · Bryan Kian Hsiang Low · Patrick Jaillet