Federated Learning: Bridging Data Privacy and AI Advancements
D. Sumathi, Likitha Chowdary Botta, Mure Sai Jaideep Reddy, Avi Das - · 2024
The realm of machine learning and deep learning model training presents a paradox—the need for data to fuel intelligent systems, juxtaposed with the imperative of data privacy. In this chapter, we embark on a comprehensive exploration of the nuanced complexities surrounding data privacy in the context of machine learning. This chapter provides a deep understanding of Federated Learning, a radical approach that endeavors to harmonize the apparent contradictions. Federated Learning upholds the promise of privacy while ensuring the efficacy of AI models in a variety of applications. Throughout this chapter, one can have a profound understanding of the principles of data privacy, the benefits, and potential applications of AI models in diverse fields, and the multifarious advantages that Federated Learning brings to the forefront. Additionally, this chapter also examines various types of Federated Learning, each tailored to specific use cases and constraints. Further, discussions on the optimization algorithms that power Federated Learning have been done. Through illustrative examples and case studies, we exhibit the practical applications of Federated Learning in various domains, emphasizing its transformative impact on collaborative, secure, and privacy-respecting model training. At the end, this chapter will be providing thoughts on the remarkable advancements in AI, and the game-changing potential of Federated Learning.