## 3 Related Work
### Homogeneous Model FL  
To address the challenge of non-iid (non-independent and identically distributed) data in FL, [PERSON] et al. proposed FedProx [12]. It introduces a proximal term to the FedAvg \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Name of the Method** & **Characteristics** & **Limitations** & \\ \hline FedProx [12] & Add a proximal term to ensure that the local models of participants stay close to the global model. & It is challenging to select an appropriate center point \(\omega_{c}\) for the proximal term. & It is challenging to select an appropriate center point \(\omega_{c}\) for the proximal term. & It is challenging to select an appropriate center point \(\omega_{c}\) for the proximal term. \\ \hline FedYogi [13] & Add a gradient correction to further suppress the data heterogeneity and performance variations among participants. & The computation cost increases with the calculation of gradient corrections, \(\Delta_{\omega_{c}}\). & The computation cost increases with the calculation of gradient corrections, \(\Delta_{\omega_{c}}\). \\ \hline FedTCR [14] & 1. & Groups clients by resources. Only the group leader communicates with the server. & 3. Only considers the computing resources. & Only considers the computing resources. & Privacy issues arise. \\ \hline FedTiny [15] & Select pruned models by evaluating client datasets and further sparsify the update parameters. & The pruning procedure incurs additional computation and transmission costs. & The pruning procedure incurs additional computation and transmission costs. \\ \hline FedDF [16] & Utilize knowledge distillation to share information across different types of models. & It needs a public dataset that is unrealistic for real-world scenarios. & It needs a public dataset that is unrealistic for real-world scenarios. \\ \hline MFedAvg [11] & Distribute models of different sizes to clients, allowing each client to receive and accommodate a suitable model. & It does not consider the scenario of non-iid data. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summarizations of the characteristics and limitations of the benchmarked federated learning techniques. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multirow{2}{*}{**Method/Issues**} & **Reduce Non-iid** & **Alleviate Communication Bandwidth** & **Alleviate Computational Capability** & **Improve Transfer Speed** \\ \hline FedProx [12] & **O** & & & \\ \hline FedYogi [13] & **O** & & & \\ \hline FedTCR [14] & & & & **O** & **O** \\ \hline FedTiny [15] & **O** & & & & **O** \\ \hline FedDF [16] & **O** & **O** & & & \\ \hline MFedAvg [11] & & **O** & **O** & & \\ \hline Oort [17] & **O** & **O** & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparisons of benchmarked federated learning techniques (in which the symbol “O” denotes the issue that has been addressed in the method). algorithm, a commonly used algorithm in FL for aggregating local model updates from participant devices. The proximal term in FedProx aims to keep the local models of participants close to the global model by imposing a penalty if the local data are biased. This penalty encourages participants to contribute updates that align with the global model. Building upon FedProx, [PERSON] et al. proposed FedYogi [13]. FedYogi enhances FedProx by introducing gradient corrections. These corrections consider the performance variations among participants and adaptively adjust the importance of each participant's parameter updates to the server. By considering the individual participants' performance, FedYogi aims to better use the updates from participants with higher reliability or accurate data while reducing the impact of updates from participants with less reliable data. However, calculating the gradient corrections in FedYogi can introduce additional computation overhead, potentially increasing the training time.