Federated Learning: Security and Privacy

As data becomes more abundant and widespread across personal devices, the need for secure, privacy-aware machine learning is growing.Federated Learning (FL) offers a promising solution, enabling smart devices to collaboratively train models without sharing raw data.Yet, despite its benefits, FL faces serious risks from poisoning and inference attacks. This book begins by introducing the fundamentals of machine learning, along with core deep learning architectures.Based on this foundation, it introduces the concept of Federated Learning (FL), which is a decentralised approach that enables collaborative model training without sharing raw data.The book provides an in-depth exploration of FL’s various forms, system architectures, and practical applications.A significant emphasis is placed on the growing security and privacy concerns in FL, particularly poisoning (both data poisoning and model poisoning) and inference attacks.It discusses state-of-the-art mitigation strategies, such as Byza