## 1 Introduction  
### Background  
The trends of advanced internet applications have had an overwhelming impact, particularly with the introduction of numerous machine learning (ML) algorithms. These algorithms have exhibited immense potential for a wide range of real-world applications. However, the success of these applications relies heavily on the establishment of a trust and secure paradigm. According to Gartner investigations [1], it is predicted that, by 2024, 75% of the global population will have their personal data covered under privacy regulations. Without this foundation trust and security, the future internet and digital economy, with their unlimited potential, will always be underestimated. To address the security concerns in federated environments--including the inherent dilution of the internet among mass usage, common vulnerabilities stemming from the Internet of Things, identity authentication, and significant digital fragmentation--various isolated and separate algorithms have been developed. However, these algorithms have proven insufficient.  
The performance of machine learning algorithms depends on access to large amounts of data for training. In traditional machine learning, training is centrally held by one organization that has access to the whole training dataset. In practice, data are often distributed across multiple parties, and sharing it for training purposes is not simple due to privacy policies and regulations like the General Data Protection Regulation [2]. These regulations impose strict rules about how data can be shared and processed between organizations.  
Due to these factors, federated learning (FL) has become a hot research topic on machine learning since its emergence on 2016 [3]. This promising technique allows multiple parties to jointly train a global model by only exchanging updates about local models and without the need to share their private datasets. This offers a promising solution to mitigate the potential privacy leakage of sensitive information about individuals. Recent works have demonstrated that FL may not always provide privacy and robustness guarantees [4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. While the private data never leaves their owner, the exchanged models are prone to memorization of the private training dataset. Some sensitive information may be inferred from the shared information using some well-known attacks like gradient inversion, reconstruction attacks, membership inference, and property inference attacks.  
One way to mitigate this type of attacks is to use privacy preserving techniques like differential privacy (DP) and homomorphic encryption (HE). Differential privacy offers a way to disrupt data while preserving the statistical properties of the data. This allows us to have meaningful analysis and statistics while countering some of previous attacks. On the other hand, homomorphic encryption allows for conducting computation on encrypted data and then decrypting only the result. This allows FL to access the aggregation of gradient without accessing the gradient themselves.  
Each technique has its own advantages and limitations. In this paper, we focus on the different works of the literature that use HE and DP techniques in federated learning context. We aim to analyze the advantages and the limitations of each technique taken alone before addressing the combination of the two techniques.