# An Improved Federated Learning-Assisted Data Aggregation Scheme for Smart Grids
## 8 Evaluation
Regardless of any variations in the number of users, the transmitted data volume demonstrates a linear increase as the number of user gradients rises. Additionally, Table 1 reveals that the communication overhead of users within the system primarily stems from the masking and verification stages, where \(Num\) refers to the number of clients, \(DP\) means dropout, \(SK\) stands for share keys, \(MI\) denotes masking, \(UA\) marks user authentication, \(SA\) represents secure aggregation, and \(CR\) denotes correctness verification. Figure 10 presents a visualization of the computation overhead experienced by the server throughout the system's execution. Contrary to the variation pattern observed in the computation overhead of users, the server exhibits a linear correlation with both the number of local gradients held by users and the number of users. As the number \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Client** & **Server** & **Server** & **Client** & **Server** & **Server** \\ \hline Num & 100 & 100 & 100 & 300 & 300 & 300 \\ DP & 0\% & 0\% & 30\% & 0\% & 0\% & 30\% \\ AK & 100 & 1 & 1 & 100 & 5 & 5 \\ SK & 1502 & 203 & 198 & 13,638 & 347 & 325 \\ MI & 4250 & 560 & 524 & 7425 & 821 & 789 \\ UA & 980 & 248 & 244 & 1452 & 274 & 252 \\ SA & 532 & 1432 & 9648 & 841 & 2680 & 23,167 \\ CR & 6712 & 0 & 0 & 8521 & 0 & 0 \\ Total & 14,076 & 2444 & 10615 & 21,977 & 4127 & 24,538 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Communication overhead in each stage (ms). Figure 8: Total running overhead for each user. Figure 9: Total transmitted data for each user. of gradients or users increases, the server's computation overhead rises correspondingly. Furthermore, Table 1 reveals that the server's computation overhead is greatly influenced by the number of users who withdraw from the system. Overall, the primary sources of overhead for the \(CS\) during system execution are the aggregation of gradients transmitted by users and the removal of masks. The algorithm proposed in this paper and its comparison with existing federated learning algorithms in terms of functionality are shown in Table 2. The comparison indicators include transmission process protection (TPP), intermediate results protection (IRP), support for exit (SE), identity recognition (IR), and results verification (RV). In the table, the presence of \"Y\" indicates the availability of the feature in the corresponding scheme, while \"N\" denotes its absence. All algorithms in this table achieve transmission process protection in federated learning. [PERSON] et al. 's algorithm, for the first time in the federated learning framework, introduces a privacy-preserving approach by adding random vectors to transmitted data to protect data privacy and using digital signature technology for identity identification. [PERSON] et al. 's algorithm uses homomorphic encryption to protect the aggregation intermediate results in the federated learning environment and achieves identity verification through zero-knowledge proof algorithms. [PERSON] et al. 's algorithm provides authentication for the correctness of aggregation results while protecting transmitted data and implementing identity identification using hash functions. [PERSON] et al. 's algorithm combines federated learning with differential privacy, making it difficult for attackers to infer the original data, thus protecting the intermediate results of federated learning. This table shows that our proposed algorithm successfully achieves the aforementioned functionalities, affirming the efficacy of our approach. Figure 11 presents a comparison between the proposed approach and the federated learning algorithm in terms of time consumption. In the comparative experiment, the federated learning environment was uniformly configured with the possibility of the mid-way node dropout. In our proposed approach, additional phases were introduced compared to the algorithms presented by [PERSON] and [PERSON] et al.