## 5 Experiments and Results
### Results
The threshold for the f1-score is reached at communication round 4 for both data types. Overall, the performance of both FedAVGP2P and FedSGD2P with Heuristic 0 and a varying fraction of clients indicates that while the fraction of client adjustments might slightly affect the speed of convergence to the threshold f1-score, the algorithms are generally capable of reaching the desired performance level regardless of the C setting, especially in IID data scenarios. In non-IID scenarios, where client data are more heterogeneous, the choice of C appears to be more critical. Figure 10a shows that the performance of the model performs consistently across different values of fraction of clients C when the data are IID. The convergence towards the f1-score threshold is smooth, indicating that the heuristic and IID assumptions work well together. However, there is a notable performance variance with different 'C' values, suggesting that 'C' impacts the learning process when data are uniformly distributed. From Figure 10b, we can see that the threshold is crossed when the round is 4, just as  
Figure 9: Execution of FedAVGP2P and FedSGD2P with f1-score threshold set at 0.9 and Heuristic 0 with varying fraction of clients C.  
for the previous one. Figure 10c is executed under IID conditions and shows a slightly erratic convergence, potentially indicating a greater sensitivity to the stochastic nature of the algorithm. The choice of C affects the rate of convergence, implying its role as a tuning parameter for balancing communication efficiency and model performance. Finally, Figure 10d reveals a more pronounced effect of C values on performance. It also shows that the threshold is reached quicker at approximately the communication round equal to 3. Figure 11 explores the performance when Heuristic H2 is applied to the FedAVG2P and FedSGD2P algorithms across different fractions of clients (denoted as 'C') in both IID and non-IID settings. Figure 11a suggests that when data are identically and independently distributed, the FedAVGP2P algorithm exhibits stable performance across varying fractions of client participation. Interestingly, the performance difference between the various C values is marginal, indicating that Heuristic H2 may enable the algorithm to leverage information effectively, even with lower client participation. In Figure 11b, in contrast to IID data, the non-IID setting reveals a wider spread in performance across different C values. Both cross the threshold at approximately when the communication round is 4. Figure 11c shows the performance pattern for FedSGD2P under IID conditions with Heuristic H2. It tends to mirror that of FedAVGP2P, with a notable difference in threshold crossing at communication round at 3. The convergence of the f1-score towards the threshold, irrespective of the C value, points to a potential reduction in the necessity for high client participation. The non-IID scenario for FedSGD2P is illustrated in Figure 11d, where the threshold f1-score is crossed when the communication round is approximately 4. Figure 10: Execution of FedAVGP2P and FedSGD2P with f1-score threshold set at 0.9 and Heuristic 1. Figure 12a illustrates that the FedAVGP2P algorithm under IID conditions with Heuristic H3 shows a close convergence of f1-scores for all C values by the eighth communication round. The threshold of the f1-score is crossed when the communication round is approximately 4. The same applies for Figure 12b with the difference that the threshold is crossed when the communication round is approximately 3. Figure 12c shows that the threshold is approximately crossed when the communication round is 5. The convergence of performance across different fractions of client participation indicates that Heuristic H3 aids in efficient learning, irrespective of the exact participation rate. In Figure 12d, the graph reveals a greater spread in performance between different 'C' values, especially in the initial rounds.