The paper "Anomaly detection and defense techniques in federated learning: a comprehensive review" by Chang Zhang, Shunkun Yang, Lingfeng Mao, and Huansheng Ning provides a comprehensive overview of security and privacy anomalies in federated learning (FL) and their corresponding defense mechanisms. FL is a machine learning approach that allows multiple clients to collaboratively train a model without centralizing their data, addressing privacy concerns. However, FL faces challenges such as data poisoning, model poisoning, backdoor attacks, Byzantine attacks, Sybil attacks, free-riding, and inference attacks. The paper categorizes these anomalies into client, server, and communication perspectives, detailing the types of attacks and the methods used to detect and defend against them. It also discusses the impact of non-independent identically distributed (non-IID) data on anomaly detection and defense, and proposes novel classification methods to improve the effectiveness of defenses. The paper aims to provide a systematic review of FL security and privacy research to help researchers and practitioners better understand and apply FL in various scenarios.The paper "Anomaly detection and defense techniques in federated learning: a comprehensive review" by Chang Zhang, Shunkun Yang, Lingfeng Mao, and Huansheng Ning provides a comprehensive overview of security and privacy anomalies in federated learning (FL) and their corresponding defense mechanisms. FL is a machine learning approach that allows multiple clients to collaboratively train a model without centralizing their data, addressing privacy concerns. However, FL faces challenges such as data poisoning, model poisoning, backdoor attacks, Byzantine attacks, Sybil attacks, free-riding, and inference attacks. The paper categorizes these anomalies into client, server, and communication perspectives, detailing the types of attacks and the methods used to detect and defend against them. It also discusses the impact of non-independent identically distributed (non-IID) data on anomaly detection and defense, and proposes novel classification methods to improve the effectiveness of defenses. The paper aims to provide a systematic review of FL security and privacy research to help researchers and practitioners better understand and apply FL in various scenarios.