As federated learning utilization quickly expands to a variety of industries, examining its interactions with, and impact on, machine learning bias becomes increasingly relevant. This chapter is dedicated to the discussion of social fairness in federated learning, as opposed to fairness in equal party...
Abstract: Fairness has been considered as a critical problem in federated learning (FL). In this work, we analyze two direct causes of unfairness in FL - an unfair direction and an improper step size when updating the model. To solve these issues, we introduce an effective way to measure ...
大家好,今天说的这篇论文是2020年FL-IJCAI'20 workshop的《Collaborative Fairness in Federated Learning》,一篇很简短易懂的论文 一句话总结:这篇论文提出CFFL框架,根据参与者的声誉收敛到不同模型,实现联邦学习公平协作 论文地址在这:arxiv.org/abs/2008.1216 其他联邦学习公平性论文: 反向人:联邦学习激励机制综述...
内容提示: Federated Fairness Analytics: Quantifying Fairnessin Federated LearningOscar Dilley 1,* , Juan Marcelo Parra-Ullauri 1 , Rasheed Hussain 1 , and Dimitra Simeonidou 11 Smart Internet Lab, School of Electrical, Electronic and Mechanical Engineering, University of Bristol, UK, BS8 1UB* ...
联邦学习论文阅读笔记07 Collaborative Fairness in Federated Learning 这篇论文提出CFFL框架,根据参与者的声誉收敛到不同模型,实现联邦学习公平协作 参考笔记:https://zhuanlan.zhihu.com/p/600343559 方法:衡量声誉标准:参与方上传的梯度质量
The existing federated learning fairness methods often resort to rudimentary modifications of the global loss function or the design of sub-optimization goals. Unfortunately, these approaches often lack a clear definition of primary and secondary objective, or simply address the fairness of the model ...
Federated learning is a novel distributed learning approach that allows multiple federating agents to jointly learn a model. While this approach might reduce the error each agent experiences, it also raises questions of fairness: to what extent can the error experienced by one agent be significantly...
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Fairness and Accuracy in Federated Learning In the federated learning setting, multiple clients jointly train a model under the coordination of the central server, while the training data is kept on the client to ensure privacy. Normally, inconsistent distribution of data across different devices in ...
1.1 故事的核心内容从"Motivated by the importance and challenges of group fairness in federated learning,……"往后的内容中可知,表明作者通过提出了一种fairness-aware aggregation method解决FL中的group fairness问题。 1.2 从"We evaluate FairFed empirically versus common baselines for fair ML and federated ...