# Evaluating the Impact of Mobility on Differentially Private Federated Learning +
## 7 Discussion  
Communication costs can be a very important constraint in federated learning, as servers and participants need to communicate model parameters frequently. Existing research has taken three strategies to reduce communication overhead: an optimization algorithm, model compression, and client selection. A rational strategy is to reduce communication frequency and increase the time interval of model aggregation, which can be achieved by improving the performance of local training [9]. The authors also found that increasing local iteration time and reducing global communication rounds can increase a global model's convergence efficiency while ensuring the performance of federated learning. Compressing network traffic by reducing the overall size of the uploaded model parameters is another useful strategy. Recent works [69; 70] investigated Top-k sparsification-based compression techniques in which parameters with the largest variations (i.e., \(k\) \"important\" parameters) are selected and transmitted to a server, instead of uploading an entire local model. The authors in [71; 72] employed quantization and encoding schemes to further reduce the sizes of the \"value\" and \"index\" parts in the \(k\) parameters. Furthermore, client selection methods are logical strategies for improving communication efficiency in federated learning. [PERSON] et al. [73] studied a method that detects whether local model updates from clients match a global model's update trend to avoid sending irrelevant parameters. [PERSON] et al. [74] investigated a method to evaluate clients' model quality to select high-quality clients for federated learning. [PERSON] et al. [75] proposed a framework to prioritize client participation using data that provide the greatest utility in improving model accuracy and training speed. [PERSON] and [PERSON] [76] designed a new protocol that predefines deadlines for updating and uploading model parameters so that a server can aggregate them in a given time. The mobility-based federated learning in this work requires vehicles to share their own knowledge with a coordinating server, which adds additional communication cost. However, the server preferentially selects vehicles that have important data for model training, which enables satisfactory accuracy to be achieved in fewer communication rounds. In particular, the selection mechanism in this work is similar to that in [74], demonstrating that their framework can improve accuracy performance by 32-113% after 30 learning rounds and can converge to the global optimum with fewer participants. The convergence of federated learning has been analyzed in many studies. Several works examined the effects of data heterogeneity on convergence by assuming full client participation [55; 77; 78]. However, not all clients actually participate in each training round, and thus the authors in [79; 80; 81] analyzed federated learning with unbiased partial client \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Attack Success Rate** \\ \hline Vanilla FL, \(z=0\) & 0.77 \\ Vanilla FL, \(z=0.5\) (privacy-enhanced) & 0.68 \\ \hline Mobile FL, \(z=0\) & 0.75 \\ Mobile FL, \(z=0.5\) (privacy-enhanced) & 0.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Success rate of membership inference attack in four training models in the RPGM scenario. participation where clients are selected uniformly at random. These works show that the federated averaging (FedAvg) algorithm on non-IID datasets achieves a convergence rate of \(\mathcal{O}(1/T)\), where \(T\) is the number of communication rounds. Since the mobility-based federated learning in this work, especially in non-accident situations, takes unbiased partial client participation, its error convergence is expected to follow the same rate as shown in our experimental results in Section 6.1. In the case of car accident events, on the other hand, our learning model takes a biased client selection strategy, and recent empirical studies have shown that biased selection affects convergence speed.