# FedCO: Communication-Efficient Federated Learning via Clustering Optimization +
## 4 Proposed Approach
The experimental results on the CIFAR-10 data show that the model trained in the IID and Non-IID data settings need 200 rounds for FedAvg to reach 85% of the accuracy, while it requires 188 rounds for FedProx to reach 85% in IID, and more than 200 rounds in Non-IID to obtain target accuracy. On the other hand, FedCO and CMFL require 30 and 80 rounds, respectively, to converge under the IID data distribution scenario. Furthermore, within the Non-IID data distribution setting, the model converges to an accuracy of 85% in 200 rounds for the FedAvg, while it requires 60 and 160 communication rounds for FedCO and CMFL, respectively. Similarly, in the Non-IID data distribution setting, the FedCO communication costs are reduced to 82% with the MNIST data, 65% with the FashionMNIST data, and up to 70% with the CIFAR-10 dataset, compared to the FedAvg. Although FedProx is considered to be an optimized version of FedAvg, we can observe from the results discussed above that FedProx behaves very similarly to FedAvg and shows only a slightly better performance than FedAvg in the conducted experiments. In addition, as we mentioned earlier, our FedCO algorithm can also be interpreted as an optimized version of FedAvg. Therefore, we further study these two algorithms (FedCO and FedAvg) by conducting experiments and benchmarking their performance on two datasets from the LEAF repository, namely FEMNIST and CelebA. Figure 8 shows the final accuracy scores after several rounds of communication for the FEMNIST dataset. Comparing the results produced by the two methods, it is evident that FedCO performs significantly better than FedAvg, on both the IID and Non-IID data scenarios. Specifically, FedCO ensures a higher accuracy than that of FedAvg within a smaller number of communication rounds. For example, in Figure 8a, FedCO can reach 90% in only 110 iterations, while the FedAvg never reaches that level within 200 iterations. Analyzing the results in Figure 9, we can observe the following: (1) FedCO consistently outperforms FedAvg in both data distribution scenarios; (2) FedCO generally achieves better accuracies than FedAvg in most cases (see Figure 9b), considering that both of them have been trained with only 200 rounds. Table 5 reports the number of communication rounds that the FedAvg and FedCO algorithms need in order to converge, for the considered datasets. Specifically, the target accuracy values are 70% for FEMNIST and 65% for CelebA, respectively. In addition, FedAvg is considered as the baseline. Note that these results again verify the faster convergence of FedCO compared to that of FedAvg. Notice that we have also studied and compared FedProx and FedAvg on the same LEAF datasets, and they again have demonstrated very similar behaviors. ### Communication Overhead Analysis

In this section, we compare the efficiencies of the two compared FL algorithms for 100 communication rounds with respect to different numbers of workers on the CIFAR-10 and the MNIST datasets, under the IID and Non-IID data distribution scenarios. The obtained results are reported in Figures 10 and 11, respectively. As one can notice, the FedCO algorithm has performed significantly better than the FedAvg, FedProx, and CMFL. The reader can also observe that the communication overhead increases linearly with the number of workers. Hence, to scale in a real scenario with thousands of workers, a FL algorithm should be capable of reducing the communication cost as much as possible, and reducing the number of rounds to converge, as with the proposed FedCO algorithm. Finally, the communication overhead in the IID and Non-IID cases is very close or identical. The results produced on the FashionMNIST dataset are similar to the other two datasets. Figure 8: Learning accuracy versus number of communication rounds for FEMNIST data. The top plot presents the results produced in the case of the IID data distribution scenario, while the bottom plot depicts the results generated in the case of the Non-IID data distribution scenario. (**a**) IID; (**b**) Non-IID.