Decentralized Federated Learning (DFL) is a paradigm that combines Federated Learning (FL) with blockchain technology to enhance privacy, security, and system robustness. FL allows clients to train local models on their data without sharing raw data, while DFL eliminates the central server, reducing single points of failure and improving communication efficiency. However, DFL introduces new security challenges, such as vulnerabilities in model updates, trustworthiness of participants, and potential blockchain-related threats. This survey provides a comprehensive overview of the security and privacy aspects of DFL, including potential threats, defense mechanisms, and the role of blockchain in enhancing DFL's security and privacy. DFL operates through peer-to-peer (P2P) communication or blockchain technology, enabling clients to aggregate model parameters without relying on a central server. This decentralized approach reduces the risk of server-based attacks but introduces new challenges, such as ensuring the integrity of model updates and preventing malicious behavior by clients or miners. The survey identifies various attack types, including data poisoning, model poisoning, routing attacks, consensus attacks, and privacy-related threats like model inversion and membership inference. These attacks can compromise the performance and privacy of DFL systems. To address these challenges, the survey explores various defense mechanisms, such as homomorphic encryption, secure multiparty computation, and differential privacy. These techniques aim to protect data privacy, ensure model robustness, and prevent malicious attacks. The survey also discusses the integration of blockchain in DFL, highlighting its benefits in terms of traceability, immutability, and enhanced security. However, it also notes the potential vulnerabilities of blockchain-based systems, such as the risk of private key hijacking and 51% attacks. The survey concludes that while DFL offers significant advantages in terms of privacy and system robustness, it requires careful design and implementation to address the unique security challenges it presents. Future research directions include improving the efficiency and scalability of DFL, enhancing the security of blockchain-based systems, and developing more robust defense mechanisms against emerging threats. Overall, the survey emphasizes the importance of a balanced approach to security and privacy in DFL, ensuring that the system remains both effective and secure.Decentralized Federated Learning (DFL) is a paradigm that combines Federated Learning (FL) with blockchain technology to enhance privacy, security, and system robustness. FL allows clients to train local models on their data without sharing raw data, while DFL eliminates the central server, reducing single points of failure and improving communication efficiency. However, DFL introduces new security challenges, such as vulnerabilities in model updates, trustworthiness of participants, and potential blockchain-related threats. This survey provides a comprehensive overview of the security and privacy aspects of DFL, including potential threats, defense mechanisms, and the role of blockchain in enhancing DFL's security and privacy. DFL operates through peer-to-peer (P2P) communication or blockchain technology, enabling clients to aggregate model parameters without relying on a central server. This decentralized approach reduces the risk of server-based attacks but introduces new challenges, such as ensuring the integrity of model updates and preventing malicious behavior by clients or miners. The survey identifies various attack types, including data poisoning, model poisoning, routing attacks, consensus attacks, and privacy-related threats like model inversion and membership inference. These attacks can compromise the performance and privacy of DFL systems. To address these challenges, the survey explores various defense mechanisms, such as homomorphic encryption, secure multiparty computation, and differential privacy. These techniques aim to protect data privacy, ensure model robustness, and prevent malicious attacks. The survey also discusses the integration of blockchain in DFL, highlighting its benefits in terms of traceability, immutability, and enhanced security. However, it also notes the potential vulnerabilities of blockchain-based systems, such as the risk of private key hijacking and 51% attacks. The survey concludes that while DFL offers significant advantages in terms of privacy and system robustness, it requires careful design and implementation to address the unique security challenges it presents. Future research directions include improving the efficiency and scalability of DFL, enhancing the security of blockchain-based systems, and developing more robust defense mechanisms against emerging threats. Overall, the survey emphasizes the importance of a balanced approach to security and privacy in DFL, ensuring that the system remains both effective and secure.