Abstract

This chapter provides a brief introduction to key concepts in federated learning and analytics with an emphasis on how privacy technologies may be combined in realworld systems and how their use charts a path toward societal benefit from aggregate statistics in new domains and with minimized risk to the individuals and the organizations who are custodians of the data. After defining FL and contrasting it with traditional centralized learning, we will discuss privacy in federated technologies, examining data minimization techniques (Section 8.2) and data anonymization methods using differential privacy (Section 8.3). We will also track the practical evolution of these technologies, highlighting key production deployments. Finally, the chapter discusses Federated Analytics, which broadens FL for performing data science tasks on decentralized data (Section 8.4), and concludes by examining the open challenges and future directions for the field.