# FedCO: Communication-Efficient Federated Learning via Clustering Optimization +
## 4 Proposed Approach
Thus, we can observe that the proposed algorithm substantially reduces the accumulated communication overhead when FedCO selects only \(k\) representatives (i.e., one per cluster), rather than selecting a variable number of representatives based on a predefined threshold to train a global model. Table 7 presents how many workers per round have been selected as representatives when various thresholds are applied for CelebA under the IID and Non-IID data scenarios, respectively. We can see that until 10 communication rounds, the FedCO selects only \(k\) representatives, since there are no local models where the accuracy has reached 70% at 10 rounds. Thus, the number of representatives increases from 10 to 97 at round 12 due to the selection of all the clusters' workers, ensuring an accuracy that is equal to or above the given threshold. Notice that there are 97 workers of different clusters that reach the value of accuracy of their local models of 70% or above. We can see that FedCO needs 30 rounds to have a number of workers whose accuracy is greater than or equal to 80% and to meet this condition under IID data. Furthermore, to meet the threshold of 85%, FedCO requires 100 rounds to have a number of workers (98) such that their accuracy of the local models meets this condition under IID. On the other hand, for Non-IID, FedCO never meets this \begin{table}
\begin{tabular}{l l l} \hline \hline  & \multicolumn{2}{c}{**CelebA**} \\ \hline Threshold Accuracy & IID & Non-IID \\ \hline \(\geq\)70\% & 853 & 826 \\ \(\geq\)75\% & 673 & 515 \\ \(\geq\)80\% & 600 & 245 \\ \(\geq\)85\% & 257 & 226 \\ \hline \multicolumn{3}{c}{**FEMNIST**} \\ \hline Threshold Accuracy & IID & Non-IID \\ \hline \(\geq\)70\% & 912 & 844 \\ \(\geq\)75\% & 806 & 694 \\ \(\geq\)80\% & 730 & 604 \\ \(\geq\)85\% & 408 & 380 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The number of selected representatives with respect to four different threshold values on the LEAF datasets CelebA (top) and FEMNIST (bottom) for 100 global rounds. condition, since no local models have a accuracy value of higher than or equal to 85%; thus, FedCO selects only 36 workers to represent the different clusters. Figure 12 provides communication overheads for various thresholds. It is obvious to the reader that a higher number of selected representatives implies a higher values of communication costs to the server. The above results suggest that our proposed FedCO algorithm can substantially reduce the communication overhead by using a higher accuracy threshold. In general, FedCO can be considered as being robust to different application scenarios by being able to tune its parameters (e.g., the accuracy threshold or the number of top-ranked representatives per cluster) to find a trade-off between the application-specific resource constraints and the accuracy requirements. ## 7 Conclusions

This paper proposes a clustering-based FL approach, entitled Federated Learning using Clustering Optimization (FedCO). The proposed FedCO approach partially builds upon our previous work and extending further towards proposing a dynamic clustering scheme that improves global accuracy and that reduces the communication overhead in a Federated Learning context. The proposed approach dynamically identifies worker participants in each communication round by initially clustering the workers' local updates and selecting a representative from each cluster to communicate with the central server, thus minimizing the communication cost. The proposed FedCO method is evaluated