Federated learning (FL) as distributed machine learning has gained popularity as privacy-aware Machine Learning (ML) systems have emerged as a technique that prevents privacy leakage by building a global model and by conducting individualized training of decentralized edge clients on their own private data. The existing works, however, employ privacy mechanisms such as Secure Multiparty Computing (SMC), Differential Privacy (DP), etc. Which are immensely susceptible to interference, massive computational overhead, low accuracy, etc. With the increasingly broad deployment of FL systems, it is challenging to ensure fairness and maintain active client participation in FL systems. Very few works ensure reasonably satisfactory performances for the numerous diverse clients and fail to prevent potential bias against particular demographics in FL systems. The current efforts fail to strike a compromise between privacy, fairness, and model performance in FL systems and are vulnerable to a number of additional problems. In this paper, we provide a comprehensive survey stating the basic concepts of FL, the existing privacy challenges, techniques, and relevant works concerning privacy in FL. We also provide an extensive overview of the increasing fairness challenges, existing fairness notions, and the limited works that attempt both privacy and fairness in FL. By comprehensively describing the existing FL systems, we present the potential future directions pertaining to the challenges of privacy-preserving and fairness-aware FL systems.

翻译：联邦学习（FL）作为一种分布式机器学习技术，随着隐私感知型机器学习系统的兴起而获得广泛关注。该技术通过构建全局模型并对分散的边缘客户端在其私有数据上进行个性化训练，从而防止隐私泄露。然而，现有工作采用安全多方计算（SMC）、差分隐私（DP）等隐私机制，这些机制极易受到干扰、计算开销巨大且精度较低。随着FL系统部署日益广泛，确保公平性并维持客户端的主动参与成为挑战。极少数工作能够为大量多样化客户端提供令人满意的性能，且未能防止FL系统中对特定人群的潜在偏见。当前研究无法在隐私、公平性和模型性能之间达成平衡，且易受多种其他问题影响。本文提供全面综述，阐述了FL的基本概念、现有隐私挑战、技术手段及与FL隐私相关的代表性工作。同时，我们广泛概述了日益严峻的公平性挑战、现有公平性概念，以及少数同时兼顾隐私与公平性的FL研究。通过系统描述现有FL系统，我们提出了面向隐私保护与公平感知FL系统挑战的未来潜在研究方向。