Federated learning (FL) is a distributed machine learning strategy that enables participants to collaborate and train a shared model without sharing their individual datasets. Privacy and fairness are crucial considerations in FL. While FL promotes privacy by minimizing the amount of user data stored on central servers, it still poses privacy risks that need to be addressed. Industry standards such as differential privacy, secure multi-party computation, homomorphic encryption, and secure aggregation protocols are followed to ensure privacy in FL. Fairness is also a critical issue in FL, as models can inherit biases present in local datasets, leading to unfair predictions. Balancing privacy and fairness in FL is a challenge, as privacy requires protecting user data while fairness requires representative training data. This paper presents a "Fair Differentially Private Federated Learning Framework" that addresses the challenges of generating a fair global model without validation data and creating a globally private differential model. The framework employs clipping techniques for biased model updates and Gaussian mechanisms for differential privacy. The paper also reviews related works on privacy and fairness in FL, highlighting recent advancements and approaches to mitigate bias and ensure privacy. Achieving privacy and fairness in FL requires careful consideration of specific contexts and requirements, taking into account the latest developments in industry standards and techniques.

翻译：联邦学习（FL）是一种分布式机器学习策略，使参与者能够在不共享各自数据集的情况下协作训练共享模型。隐私和公平性是联邦学习中的关键考量。尽管联邦学习通过最小化存储在中央服务器上的用户数据量来促进隐私保护，但其仍存在需要解决的隐私风险。为确保联邦学习的隐私性，业界遵循差分隐私、安全多方计算、同态加密和安全聚合协议等标准。公平性同样是联邦学习中的重要问题，因为模型可能继承本地数据集中的偏差，导致不公平的预测结果。在联邦学习中平衡隐私与公平性是一项挑战，因为隐私需要保护用户数据，而公平性则需要代表性的训练数据。本文提出了一种"公平差分隐私联邦学习框架"，旨在解决无验证数据情况下生成公平全局模型以及创建全局私有差分模型的挑战。该框架采用裁剪技术处理有偏模型更新，并利用高斯机制实现差分隐私。本文还综述了联邦学习中关于隐私与公平性的相关研究，重点介绍了近期进展以及减轻偏差和确保隐私的方法。在联邦学习中实现隐私与公平性需要结合具体场景和要求进行仔细考量，同时兼顾行业标准与技术的最新发展。