## 2 Related Work
### Privacy-Preserving Techniques
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Focus Area** & **Methodology** & **Key Findings** & **Limitations** \\ \hline \multirow{4}{*}{[8]} & \multirow{4}{*}{Introduction to FL} & Proposed idea of FL and its application in mobile and edge devices & Demonstrated FL’s applicability in various scenarios, including healthcare, for training models without data sharing & No privacy mechanisms; not adapted to healthcare or non-IID data \\ \hline \multirow{4}{*}{[9]} & \multirow{4}{*}{FL in EHRs for healthcare} & Applied FL to Electronic Health Records (EHRs) for predictive modeling of diseases & Showed potential for cardiovascular and cancer prediction using FL, but faced issues of scalability and computational costs & Scalability and privacy not addressed; high computational cost \\ \hline \multirow{4}{*}{[10]} & \multirow{4}{*}{Optimizations in IoT and big data} & Proposed FLBM-IoT and PPFLB models for minimizing communication and enhancing model performance & Improved FL in IoT, but single-tier aggregation was limiting for large healthcare networks & Lacked hierarchical design; no DP or SMPC integration \\ \hline \multirow{4}{*}{[12]} & \multirow{4}{*}{Privacy concerns in FL} & Discussed limitations of traditional data masking and encryption techniques & Emphasized need for advanced privacy-preserving methods due to modern threat scenarios & Theoretical only; lacked implementation or validation \\ \hline \multirow{4}{*}{[19]} & \multirow{4}{*}{Intellectual property rights in FL} & Proposed FedIPR for embedding watermarks in FL models to verify ownership & Provided solutions for safeguarding intellectual property in federated models & Focused on IP; did not address privacy, scalability, or communication \\ \hline \multirow{4}{*}{[20]} & \multirow{4}{*}{Privacy-preserving FL} & Introduced techniques like Additive Homomorphic Encryption and Byzantine-tolerant aggregation & Improved accuracy and privacy in FL by addressing model poisoning attacks with minimal overhead & High computational cost; limited healthcare applicability \\ \hline \multirow{4}{*}{[21]} & \multirow{4}{*}{Hybrid FL model for NG-IoT} & Combined synchronous and asynchronous operations with Two-Trapdoor Homomorphic Encryption (TTHE). & \multirow{4}{*}{Addressed user dropout and low-quality data issues in next-generation IoT settings} & High overhead; not applicable to healthcare networks \\ \hline \multirow{4}{*}{[22]} & \multirow{4}{*}{Privacy-preserving FL framework} & Combined advanced techniques like SMPC and Differential Privacy for secure decentralized model training & Outperformed traditional methods in accuracy, computational efficiency, and privacy & No dynamic control of privacy–utility trade-off \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related literature with reference, methodology used, and key findings. The process included the application of a Gaussian Mixture Model and Mahalanobis Distance for Byzantine-tolerant aggregation. In order to preserve confidentiality and at the same time reduce computational and communication costs, the model used Additive Homomorphic Encryption. The results indicated that the proposed model provided better accuracy and privacy than the state-of-the-art methods and encryption schemes, including Fully Homomorphic Encryption and Two-Trapdoor Homomorphic Encryption. In addition, the proposed model also solved the problem of identifying maliciously encrypted, non-independent, and identically distributed gradients with low cost of computation and communication. FL has been considered as a privacy-preserving (PP) solution; yet, the current research has shown that there are still some privacy issues and threats, particularly in NG-IoT environments. Research revealed that attackers on a server had a possibility of retrieving information like data attributes and memberships.