## 1 Introduction
### Contribution  
Our paper makes a notable contribution by thoroughly exploring and examining various scholarly sources. The primary scope of this paper revolves around addressing privacy concerns in federated learning. Consequently, certain related issues, such as communications, systems heterogeneity, and statistical heterogeneity, are intentionally excluded from our focus. Within the realm of privacy preservation, our main emphasis lies on exploring and analyzing differential privacy (DP) and homomorphic encryption (HE) techniques. While we acknowledge the existence of other privacy techniques in the literature, such as anonymization, secure multi-party computation, and blockchain, we do not directly delve into them in this paper. By narrowing our focus to DP and HE techniques, we can provide a more detailed and comprehensive analysis of their capabilities and limitations in the context of privacy preservation. This approach allows us to deliver a focused and valuable contribution to the research community and promotes a deeper understanding of the pivotal role these techniques play in ensuring secure and privacy-aware federated learning systems.  
The main contributions of this paper are summarized as follows:  
1. We scrutinize the array of research addressing privacy-related attacks in federated learning (FL), demonstrating the practicality and real-world relevance of these threats, highlighting their potential implications in distributed learning environments. Our primary focus lies on privacy attacks, where we delve into various techniques that adversaries can employ to compromise the privacy and security of FL systems.
2. We delve into the role of differential privacy (DP) in FL, detailing its deployment across various settings: central differential privacy (CDP), local differential privacy (LDP), and the shuffle model. By providing a comprehensive analysis of these DP deployment settings, we offer insights into the strengths, limitations, and practical implications of each approach.
3. We investigate the application of homomorphic encryption (HE) as a powerful tool to enhance privacy within FL. Our primary focus is on countering privacy attacks and safeguarding sensitive data during the collaborative learning process. Through our investigation, we provide valuable insights into the capabilities and limitations of homomorphic encryption in FL.
4. We examine the body of research that explores the fusion of homomorphic encryption (HE) and differential privacy (DP) in the context of federated learning (FL). Our primary objective is to shed light on the motivations behind such integrations and understand the potential benefits they offer in enhancing privacy and security in distributed learning environments.  
The rest of this paper unfolds as follows: Section 2 provides essential background knowledge on HE, DP, and FL. Section 3 delves into various privacy attacks within the FL framework. Section 4 discusses the combination of DP with FL, while Section 5 explores the use of HE for protecting privacy. In Section 6, this paper explores the combined use of HE and DP, emphasizing the potential benefits of this fusion. Section 7 is dedicated to the discussion of the results, offering deeper insight into our findings. Finally, Section 8 presents our conclusions and proposes directions for future research.