A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning
University of Singapore（新加坡大学）；Ant financial（蚂蚁金融）
Xinyi Xu, Lingjuan Lyu
备注：
Accepted as Oral presentation at International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2021 (FL-ICML'21)
Federated learning (FL) is an emerging practical framework for effective and scalable machine learning among multiple participants, such as end users, organizations and companies. However, most existing FL or distributed learning frameworks have not well addressed two important issues together: collaborative fairness and adversarial robustness (e.g. free-riders and malicious participants). In conventional FL, all participants receive the global model (equal rewards), which might be unfair to the high-contributing participants. Furthermore, due to the lack of a safeguard mechanism, free-riders or malicious adversaries could game the system to access the global model for free or to sabotage it. In this paper, we propose a novel Robust and Fair Federated Learning (RFFL) framework to achieve collaborative fairness and adversarial robustness simultaneously via a reputation mechanism. RFFL maintains a reputation for each participant by examining their contributions via their uploaded gradients (using vector similarity) and thus identifies non-contributing or malicious participants to be removed. Our approach differentiates itself by not requiring any auxiliary/validation dataset. Extensive experiments on benchmark datasets show that RFFL can achieve high fairness and is very robust to different types of adversaries while achieving competitive predictive accuracy.
National University of Singapore（新加坡国立大学）；Wuhan University（武汉大学）
In current deep learning paradigms, local training or the Standalone framework tends to result in overfitting and thus poor generalizability. This problem can be addressed by Distributed or Federated Learning (FL) that leverages a parameter server to aggregate model updates from individual participants. However, most existing Distributed or FL frameworks have overlooked an important aspect of participation: collaborative fairness. In particular, all participants can receive the same or similar models, regardless of their contributions. To address this issue, we investigate the collaborative fa…
Fairness-Aware Client Selection for Federated Learning
Nanyang Technological University (NTU)（南洋理工大学）；Alibaba-NTU Singapore Joint Research Institute（阿里巴巴-新加坡南洋理工大学联合研究院）；Alibaba Group（阿里巴巴集团）；University of Science and Technology of China（中国科学技术大学）
Enhancing Federated Survival Analysis through Peer-Driven Client Reputation in Healthcare
Department of Computer Science, Missouri University of Science and Technology（计算机科学系，密苏里科学与技术大学）；Department of Mathematical Sciences, University of Alabama in Huntsville（数学科学系，阿拉巴马大学亨茨维尔分校）
Etsy Inc（Etsy公司）；Rutgers University（罗格斯大学）；University at Albany, SUNY（纽约州立大学奥尔巴尼分校）
Recently, there has been an increasing adoption of differential privacy guided algorithms for privacy-preserving machine learning tasks. However, the use of such algorithms comes with trade-offs in terms of algorithmic fairness, which has been widely acknowledged. Specifically, we have empirically observed that the classical collaborative filtering method, trained by differentially private stochastic gradient descent (DP-SGD), results in a disparate impact on user groups with respect to different user engagement levels. This, in turn, causes the original unfair model to become even more biased…
Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift
The Pennsylvania State University（宾夕法尼亚州立大学）；State University of New York at Albany（纽约州立大学阿尔巴尼分校）；University of Minnesota Twin Cities（明尼苏达大学双城分校）；Dalian University of Technology（大连理工大学）
Federated learning (FL) is a privacy-preserving learning technique that enables distributed computing devices to train shared learning models across data silos collaboratively. Existing FL works mostly focus on designing advanced FL algorithms to improve the model performance. However, the economic considerations of the clients, such as fairness and incentive, are yet to be fully explored. Without such considerations, self-motivated clients may lose interest and leave the federation. To address this problem, we designed a novel incentive mechanism that involves a client selection process to re…
National University of Singapore（新加坡国立大学）；The University of Melbourne（墨尔本大学）；IBM Singapore Lab（IBM新加坡实验室）；Monash University（莫纳什大学）；Deakin University（迪肯大学）
This paper firstly considers the research problem of fairness in collaborative deep learning, while ensuring privacy. A novel reputation system is proposed through digital tokens and local credibility to ensure fairness, in combination with differential privacy to guarantee privacy. In particular, we build a fair and differentially private decentralised deep learning framework called FDPDDL, which enables parties to derive more accurate local models in a fair and private manner by using our developed two-stage scheme: during the initialisation stage, artificial samples generated by Differentia…
University College London（伦敦大学学院）；Duke University（杜克大学）；Apple Inc.（苹果公司）
Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models. In this work, we study minimax group fairness in federated learning scenarios where different participating entities may only have access to a subset of the population groups during the training phase. We formally analyze how our proposed group fairness objective differs from existing federated learning fairness criteria that impose similar performance across participants instead of demographic groups. We provide an optimization algorithm -- FedMinMax -- for so…
University College London（伦敦大学学院）；Duke University（杜克大学）
Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models. In this work, we study minmax group fairness in paradigms where different participating entities may only have access to a subset of the population groups during the training phase. We formally analyze how this fairness objective differs from existing federated learning fairness criteria that impose similar performance across participants instead of demographic groups. We provide an optimization algorithm -- FedMinMax -- for solving the proposed problem that pr…
We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative-filtering methods to make unfair predictions for users from minority groups. We identify the insufficiency of existing fairness metrics and propose four new metrics that address different forms of unfairness. These fairness metrics can be optimized by adding fairness terms to the learning objective. Experiments on synthetic and real data show that our new metrics can better measure fairness than the baseline, and that the fai…
Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing
Knight Foundation School of Computing and Information Sciences, Florida International University (FIU)（佛罗里达国际大学奈特基金会计算与信息科学学院）；School of Computing, Southern Illinois University（南伊利诺伊大学计算学院）；Enterprise and Logistics Engineering program at FIU（佛罗里达国际大学企业与物流工程项目）
FAIRVAR: Fair Federated Learning via Variance Regularization
Department of Computing Science, Umeå University, Umeå, Sweden（计算机科学系，乌梅大学，乌梅，瑞典）；Department of Mathematics and Mathematical Statistics, Umeå University, Umeå, Sweden（数学与统计学系，乌梅大学，乌梅，瑞典）
Fairness-Aware Federated Learning with Trajectory Shapley Value
Faculty of Mathematics, Ecole Normale Supérieure Paris-Saclay（巴黎-萨克雷大学数学系）；Chair for Dynamics, Control, Machine Learning and Numerics – Alexander von Humboldt Professorship, Department of Mathematics, Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔兰根-纽伦堡弗里德里希-亚历山大大学数学系）
Federated Learning (FL) is a distributed machine learning protocol that allows a set of agents to collaboratively train a model without sharing their datasets. This makes FL particularly suitable for settings where data privacy is desired. However, it has been observed that the performance of FL is closely related to the similarity of the local data distributions of agents. Particularly, as the data distributions of agents differ, the accuracy of the trained models drop. In this work, we look at how variations in local data distributions affect the fairness and the robustness properties of the…
Training ML models which are fair across different demographic groups is of critical importance due to the increased integration of ML in crucial decision-making scenarios such as healthcare and recruitment. Federated learning has been viewed as a promising solution for collaboratively training machine learning models among multiple parties while maintaining the privacy of their local data. However, federated learning also poses new challenges in mitigating the potential bias against certain populations (e.g., demographic groups), as this typically requires centralized access to the sensitive …
University of British Columbia（不列颠哥伦比亚大学）；Huawei Technologies Canada Co.（华为技术加拿大公司）；Simon Fraser University（西蒙弗雷泽大学）；KTH Royal Institute of Technology（KTH皇家理工学院）
Federated learning is an emerging decentralized machine learning scheme that allows multiple data owners to work collaboratively while ensuring data privacy. The success of federated learning depends largely on the participation of data owners. To sustain and encourage data owners' participation, it is crucial to fairly evaluate the quality of the data provided by the data owners and reward them correspondingly. Federated Shapley value, recently proposed by Wang et al. [Federated Learning, 2020], is a measure for data value under the framework of federated learning that satisfies many desired …
FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs
National Engineering Laboratory for Big Data Analytics (NEL-BDA)（大数据分析国家工程实验室）；School of Mathematics and Statistics（数学与统计学学院）；Ministry of Education Key Lab for Intelligent Networks and Network Security（教育部智能网络与网络安全重点实验室）；School of Computer Science and Technology（计算机科学与技术学院）
Entropy-driven Fair and Effective Federated Learning
School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）理工程学院）；School of Science Information and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）；Department of Electrical Engineering and Computer Sciences, University of California, Berkeley（加州大学伯克利分校电气工程与计算机科学系）
Federated learning is a distributed and privacy-preserving approach to train a statistical model collaboratively from decentralized data of different parties. However, when datasets of participants are not independent and identically distributed (non-IID), models trained by naive federated algorithms may be biased towards certain participants, and model performance across participants is non-uniform. This is known as the fairness problem in federated learning. In this paper, we formulate fairness-controlled federated learning as a dynamical multi-objective optimization problem to ensure fair p…
Fairness-Aware Job Scheduling for Multi-Job Federated Learning
Nanyang Technological University（南洋理工大学）；DSO National Laboratories（DSO国家实验室）；Alibaba Group（阿里巴巴集团）；Alibaba-NTU Singapore Joint Research Institute（阿里巴巴-新加坡南洋理工大学联合研究院）
Tel-Aviv University（特拉维夫大学）；The Open University（开放大学）
The performance of machine learning algorithms can be considerably improved when trained over larger datasets. In many domains, such as medicine and finance, larger datasets can be obtained if several parties, each having access to limited amounts of data, collaborate and share their data. However, such data sharing introduces significant privacy challenges. While multiple recent studies have investigated methods for private collaborative machine learning, the fairness of such collaborative algorithms was overlooked. In this work we suggest a feasible privacy-preserving pre-process mechanism f…
Nanyang Technological University（南洋理工大学）；Alibaba-NTU Singapore Joint Research Institute（阿里巴巴-南洋理工大学新加坡联合研究院）；Alibaba Group（阿里巴巴集团）；The University of British Columbia（不列颠哥伦比亚大学）
Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning
CISUC/LASI - Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra（科瓦姆布拉大学信息与系统中心/实验室，信息工程系，科瓦姆布拉大学）；INESC TEC and the Faculty of Economy, University of Porto（INESC TEC和波尔图大学经济学院）；Institute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra（系统与机器人研究所，电子与计算机工程系，科瓦姆布拉大学）
FedPF: Accurate Target Privacy Preserving Federated Learning Balancing Fairness and Utility
The School of Computer Science, Shanghai Jiao Tong University, Shanghai, China（上海交通大学计算机科学学院，上海，中国）；The Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, Shanghai, China（上海信息安全集成管理技术重点实验室，上海，中国）；The School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China（香港中文大学（深圳）科学与工程学院，中国）
GuardFed: A Trustworthy Federated Learning Framework Against Dual-Facet Attacks
School of Artificial Intelligence and Computer Science, Nantong University（人工智能与计算机科学学院，南通大学）；School of Electrical and Computer Engineering, The University of Sydney（电气与计算机工程学院，悉尼大学）；School of Computer Science, University of St Andrews（计算机科学学院，圣安德鲁斯大学）；Faculty of Data Science, City University of Macau（数据科学学院，澳门城市大学）；Department of Electrical and Computer Engineering, University of Alberta（电气与计算机工程学院，阿尔伯塔大学）
Incentivizing Truthfulness and Collaborative Fairness in Bayesian Learning
Department of Computer Science, National University of Singapore, Singapore（新加坡国立大学计算机科学系）；Research (A STAR), Singapore（新加坡A*STAR研究）；Department of Electrical Engineering（电气工程系）；Computer Science, Massachusetts Institute of Technology, USA（美国麻省理工学院计算机科学系）
Fairis: Fairness-Aware Aggregation with Provable Influence Containment against Fairness Poisoning Attacks in Collaborative Machine Learning
Bowie State University（鲍伊州立大学）；Télécom SudParis（南巴黎电信学院）；Institut Polytechnique de Paris（巴黎综合理工学院）；Brown University（布朗大学）；Northern Virginia Community College（北弗吉尼亚社区学院）
Eindhoven University of Technology（埃因霍温理工大学）；DePaul University（德保罗大学）；University of Colorado Boulder（科罗拉多大学博尔德分校）
Research on fairness in machine learning has been recently extended to recommender systems. One of the factors that may impact fairness is bias disparity, the degree to which a group's preferences on various item categories fail to be reflected in the recommendations they receive. In some cases biases in the original data may be amplified or reversed by the underlying recommendation algorithm. In this paper, we explore how different recommendation algorithms reflect the tradeoff between ranking quality and bias disparity. Our experiments include neighborhood-based, model-based, and trust-aware…
FairSTG: Countering performance heterogeneity via collaborative sample-level optimization
University of Science and Technology of China (USTC)（中国科学技术大学）；Suzhou Institute for Advanced Research, USTC（中国科学技术大学苏州高等研究院）；Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences（中国科学院地理科学与资源研究所）；University of Chinese Academy of Sciences（中国科学院大学）
