# FedRDS: Federated Learning on Non-IID Data via Regularization and Data Sharing
## 2 Related Works  
FL has become a wide-ranging topic across machine learning [13], cybersecurity [14], industrial IoT [15], medicine intelligence [16], etc. FedAvg performs weighted parameter averaging to update parameters from multiple clients, effectively reducing communication rounds and improving the efficiency of joint learning. However, the research in [11] indicates that the difference in client weight caused by non-IID data is the root cause of FedAvg's performance degradation. Some work attempts to reduce differences in client updates to accelerate convergence speed. FedProx [4] adds proximal regularization to the local model to overcome system heterogeneity, thus enhancing stability. [PERSON] et al. [17] suggest that tackling the client drift problem can make the FL model converge faster. To reduce local drift on the global target, they propose the FedDC algorithm, which utilizes drift decoupling correction. [PERSON] et al. [18] proposed FedFa: the aggregation weights are assigned to each client at each round of global iteration based on the accuracy of client training and the number of times they are selected, as a way to reduce the aggregation caused by non-IID model performance degradation. [PERSON] et al. [19] proposed a stop-and-weight balancing method to balance the non-dropper updates in each iteration, which effectively controls the weight dispersion. FedBN suggests a novel aggregation approach that mitigates feature drift of non-IID data by setting the local BN out of sync with the global parameters [20]. [PERSON] et al. [21] proposed model contrastive learning (MOON) to enhance the algorithm performance by introducing a contrastive loss function to accelerate local update speed and improve the accuracy of the FL algorithm. [PERSON] et al. proposed a FAug approach based on FL to correct the non-IID training dataset and again reduced the communication overhead via knowledge distillation (FD), while also achieving a high accuracy rate [9]. [PERSON] et al. [22] proposed 2 DP-FL to enhance the performance of privacy issues in practical applications, adding noise to achieve differential privacy. [PERSON] et al. [23] emphasized research on fairness and robustness under non-IID and developed a scalable solver to achieve fairness and robustness. [PERSON] et al. [24] used entropy calculation based on FedAvg and proposed the FedAvg-BE algorithm, which reduced the data processing workload. Another method, pFedMe [25], breaks down personal model optimization from global model learning via the Moreau envelope function, so pFedMe can update the global model as FedAvg while also optimizing personalized models in parallel based on the local data distribution of each client, thereby improving the model performance. [PERSON] et al. [5] introduced multi-task learning in FL and proposed the MOCHA algorithm, which allows the local training models to maintain the same structure and use an alternate optimization algorithm [26] to resolve the optimal value. Table 1 lists the comparison of related methods. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Author** & **Year** & **Characteristics** & **Neural Networks** & **Personalization** & **Data Change** & **Effect** \\ \hline FedAvg & [PERSON] et al. [3] & 2017 & SGD & CNN & N & N & Reduce communication rounds by 10 to 100\(\times\) \\ \hline FedProx & Li et al. [4] & 2020 & Proximal term & ANN & Y & N & Improved testing accuracy by 20\% \\ \hline FedDC & [PERSON] et al. [17] & 2022 & Local drift variable & FCN/CNN & N & Y & Improve performance and accelerate convergence \\ \hline FedFa & [PERSON] et al. [18] & 2021 & Ordered dropout & ResNet18/CNN/RNN & Y & Y & Performance improvement \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of related methods. Some works consider the impact of network and communication resources of clients. [PERSON] et al.