Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy

Abstract

Federated learning holds great promise in learning from fragmented sensitive data and has revolutionized how machine learning models are trained. This article provides a systematic overview and detailed taxonomy of federated learning. We investigate the existing security challenges in federated learning and provide a comprehensive overview of established defense techniques for data poisoning, inference attacks, and model poisoning attacks. The work also presents an overview of current training challenges for federated learning, focusing on handling non-i.i.d. data, high dimensionality issues, heterogeneous architecture, and discusses several solutions for the associated challenges. Finally, we discuss the remaining challenges in managing federated learning training and suggest focused research directions to address the open questions. Potential candidate areas for federated learning, including IoT ecosystem, healthcare applications, are discussed with a particular focus on banking and financial domains. Landscape of Federated Learning 1.1 Need for Federated LearningData has always been of significant priority for businesses of all sizes, especially in Financial Services. With the advancement of technology, companies tend to capture customer data from many sources, such as tracking customer's activities and appending other data sources to proprietary sources to enhance their ability to contextualize data and draw new insights. However, preserving customer data privacy is vital considering the sensitive nature of customer data. (Hahn, Munir, and Mohanty 2018;Yang et al. 2019).Several factors have driven the global consensus to focus on preserving data privacy and security of individuals, businesses, and societies. As a result, governments are strengthening data security and privacy protection measures. For example, General Data Protection Regulations (GDPR) aims to protect the user's privacy by requiring operants to distinctly obtain user consent and adhere to sufficient data privacy requirements (Khan et al. 2019). The establishment of these laws and regulations poses new challenges to the traditional data processing mode of Artificial