# FedCO: Communication-Efficient Federated Learning via Clustering Optimization +
## 4 Proposed Approach
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**IID**} & \multicolumn{4}{c}{**Non-IID**} \\ \cline{2-10}
**Round** & \(\geq\)**70\%** & \(\geq\)**75\%** & \(\geq\)**80\%** & \(\geq\)**85\%** & \(\geq\)**70\%** & \(\geq\)**75\%** & \(\geq\)**80\%** & \(\geq\)**85\%** \\ \hline
1 & 10 & 10 & 10 & 10 & 10 & 10 & 10 & 10 \\
10 & 10 & 12 & 16 & 14 & 14 & 16 & 18 & 16 \\
12 & 97 & 13 & 16 & 16 & 14 & 18 & 22 & 18 \\
20 & 102 & 103 & 20 & 20 & 104 & 26 & 25 & 24 \\
30 & 105 & 104 & 95 & 18 & 104 & 30 & 28 & 24 \\
40 & 102 & 108 & 98 & 20 & 110 & 32 & 30 & 26 \\
50 & 106 & 108 & 100 & 22 & 112 & 96 & 32 & 28 \\
60 & 106 & 110 & 104 & 24 & 114 & 99 & 34 & 28 \\
70 & 107 & 111 & 106 & 26 & 116 & 102 & 36 & 32 \\
80 & 108 & 114 & 108 & 22 & 120 & 104 & 38 & 35 \\
90 & 110 & 116 & 110 & 26 & 118 & 106 & 38 & 34 \\
100 & 112 & 116 & 112 & 98 & 122 & 108 & 97 & 36 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Total number of selected representatives when the given accuracy threshold is reached in CelebA dataset at different rounds. Figure 12: The communication overhead for 100 rounds for the two LEAF datasets. The top plot presents the results produced for CelebA dataset, while the bottom plot depicts the results generated on FEMNIST dataset. (**a**) CelebA; (**b**) FEMNIST. and benchmarked to three other state-of-the-art FL algorithms (FedAvg, FedProx, and CMFL) on five publicly available and widely exploited datasets for studying distributed ML algorithms. The experimental results have shown that the proposed FedCO algorithm significantly reduces communication rounds without sacrificing accuracy. In addition, the experimental evaluation has demonstrated that our FedCO algorithm outperforms the three other FL algorithms under the two studied data distribution scenarios. We have also shown that the FedCO algorithm can dynamically adapt the workers' partitioning at each communication round by relocating the representative workers and conducting the cluster splitting needed for the clustering improvement. Our future plans include the enhancement of the FedCO approach through using other data distillation techniques; e.g., an interesting future direction could be made by applying computational topology methods for studying data topology and selecting representatives based on this. Another direction is the translation of the FedCO concept to unsupervised learning settings, i.e., developing a resource-efficient FL algorithm based on the unsupervised ML model. Conceptualization, A.A.A.-S., V.B. and E.C. ; methodology, A.A.A.-S., V.B. and E.C. ; software, A.A.A.-S.; validation, A.A.A.-S., V.B. and E.C. ; formal analysis, A.A.A.-S. and V.B. ; investigation, A.A.A.-S., V.B. and E.C. ; data curation, A.A.A.-S.; writing--original draft preparation, A.A.A.-S., V.B. and E.C. ; writing--review and editing, A.A.A.-S., V.B. and E.C. ; visualization, A.A.A.-S.; supervision, V.B. and E.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research received no external funding. Not applicable. Not applicable. Not applicable. Data Availability Statement Not applicable. The first author is supported by an Iraq Ministry of Higher Education and Scientific Research PhD Scholarship. The authors declare no conflict of interest. ## References

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