  Federated learning has been rapidly evolving and gaining popularity in recent
years due to its privacy-preserving features, among other advantages.
Nevertheless, the exchange of model updates and gradients in this architecture
provides new attack surfaces for malicious users of the network which may
jeopardize the model performance and user and data privacy. For this reason,
one of the main motivations for decentralized federated learning is to
eliminate server-related threats by removing the server from the network and
compensating for it through technologies such as blockchain. However, this
advantage comes at the cost of challenging the system with new privacy threats.
Thus, performing a thorough security analysis in this new paradigm is
necessary. This survey studies possible variations of threats and adversaries
in decentralized federated learning and overviews the potential defense
mechanisms. Trustability and verifiability of decentralized federated learning
are also considered in this study.
