# Privacy and Security in Federated Learning: A Survey
###### Abstract  
In recent years, privacy concerns have become a serious issue for companies wishing to protect economic models and comply with end-user expectations. In the same vein, some countries now impose, by law, constraints on data use and protection. Such context thus encourages machine learning to evolve from a centralized data and computation approach to decentralized approaches. Specifically, Federated Learning (FL) has been recently developed as a solution to improve privacy, relying on local data to train local models, which collaborate to update a global model that improves generalization behaviors. However, by definition, no computer system is entirely safe. Security issues, such as data poisoning and adversarial attack, can introduce bias in the model predictions. In addition, it has recently been shown that the reconstruction of private raw data is still possible. This paper presents a comprehensive study concerning various privacy and security issues related to federated learning. Then, we identify the state-of-the-art approaches that aim to counteract these problems. Findings from our study confirm that the current major security threats are poisoning, backdoor, and Generative Adversarial Network (GAN)-based attacks, while inference-based attacks are the most critical to the privacy of FL. Finally, we identify ongoing research directions on the topic. This paper could be used as a reference to promote cybersecurity-related research on designing FL-based solutions for alleviating future challenges. survey; federated learning; deep learning; machine learning; distributed learning; privacy; security; blockchain; deep learning security and privacy threats +
Footnote †: journal: Applied  
the end-user expects at least similar performances and generalization behaviors as those that originate from a centralized approach while dealing with smaller and potentially biased local data collections, i.e., non-IID (Identically and Independently Distributed) data. Then, collaborative approaches are developed to introduce communication between local learner agents to look for a robust, general model. More specifically, the recently introduced Federated Learning (FL) [3] has been subject to a growing interest to address complex problems while never sharing raw data between collaborative agents, only exchanging model parameters. As illustrated in Figure 1b,c, a variety of system infrastructures are possible. Agents can communicate in a peer-to-peer (P2P) fashion or with a centralized parameter server. Further, hierarchical structures are possible in order to consider different model aggregation scales [4]. With such an approach, sensitive data are strictly kept on the client's device, at the edge, thus initiating privacy. In addition, communication costs can be significantly reduced, since model parameters are significantly smaller than the raw data. This, however, expects edge devices to be powerful enough to conduct local learning. Nevertheless, the quantity of data is smaller than with the centralized approach, and thus allows for cheaper hardware. In conclusion, federated learning is an attractive solution for multiple application domains and technologies, from medical applications to the Internet of Things, and is subject to intensive research. Nevertheless, despite being more privacy friendly than the centralized approach, FL is still subject to attacks that may impact the learned model's relevance, as well as the data integrity. A typical example is poisoning attacks [5; 6], which aim to distort the model by sending corrupted training data. This attack type is facilitated by the centralized federated learning topology, since the parameter server cannot directly verify the data. User data integrity can also be compromised by relying on generative adversarial networks (GANs)-based attacks [7] to reconstruct data from local node models. Thus, despite the improvements compared to the centralized approaches, data security and privacy with federated learning are still burning issues that must be resolved.