## 3 Preliminaries and Definitions
### Agdoa Algorithm
#### 4.2.2 DOA Considering Greedy Strategies
The convergence characteristics of AGDOA were evaluated by setting the maximum transmission power Pmax of mobile users to 5 w with the same number of mobile users u and the number of base stations of edge servers n. The convergence characteristics of AGDOA were evaluated by setting the maximum transmission power Pmax of mobile users to 5 w. As shown in Figure 10a, when u was 12 and n was 3, AGDOA needed only 61 iterations to converge, while NCGG and GPSO needed 107 and 200 iterations, respectively; as shown in Figure 10b, when u was 40 and n is 10, AGDOA needed only 61 iterations to converge, while NCGG and GPSO needed 107 and 200 iterations, respectively; AGDOA required \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{8}{c}{**Number of Mobile Users**} \\ \cline{2-10}  & **12** & **20** & **32** & **40** & **52** & **60** & **72** & **80** & **92** & **100** \\ \hline DOA & 166 & 75 & 62 & 91 & 94 & 119 & 95 & 159 & 165 & 127 \\ ADOA & 125 & 69 & 97 & 69 & 76 & 111 & 90 & 110 & 142 & 142 \\ GDOA & 71 & 66 & 78 & 77 & 82 & 80 & 125 & 99 & 102 & 146 \\ AGDOA & 57 & 53 & 51 & 66 & 34 & 66 & 71 & 89 & 80 & 74 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The converged iteration of each algorithm under different numbers of mobile users. Figure 9: The energy consumption of different algorithms with different numbers of mobile users. only 72 iterations to converge, while NCGG and GPSO required 168 and 229 iterations, respectively. As can be seen from Figure 10a,b below, AGDOA reached convergence in fewer iterations, even with larger initial values. This indicates that our proposed AGDOA has good convergence properties. ## 7 Conclusions

In this paper, we studied a MEC network consisting of a macro BS, a set of micro BSs, and a large number of mobile users, aiming to solve two main problems in task scheduling with federated learning in a network. For the JRORS dual decision problem, we incorporated the consideration of server pricing and task completion factors to improve user-friendliness and fairness. Meanwhile, a BSSA was proposed to solve this problem based on the discrete nature of JRORS, to reduce the problem complexity. Then, for the PA problem, an AGDOA was proposed to find the optimal power allocation scheme. The simulation results validated the proposed algorithm, in which the BSSA maintained a good performance for welfare, response rate, and SSD for the JRORS problem. Compared with the heuristic algorithms NGO, BPSO, and GA, the proposed BSSA could find better solutions and obtained a higher welfare under different numbers of mobile users, workloads, and input data amounts. This was due to the addition of binaries, which reduced the complexity of the BSSA on discrete problems such as JRORS. In addition, the BSSA had a higher system response rate, and the number of tasks completed within a tolerable delay time of the request was more than the total number of requested tasks. Moreover, compared with the other heuristics compared, the BSSA paid more attention to fairness in task offloading. In addition, compared with the other algorithms, on the PA problem, each improved module of AGDOA showed a significant improvement in convergence speed and initial performance over the DOA. Compared with the optimization algorithms GPSO and NCGG, the energy consumption of the AGDOA was significantly lower under different numbers of mobile users. At the same time, the AGDOA could reach a convergence state in fewer iterations for different numbers of mobile devices. In the future, we will conduct experiments using real data, make improvements based on real applications, and apply the proposed algorithms to real applications to improve the MEC scheduling efficiency and the comprehensive level of QoS. Conceptualization, W.C.; methodology, W.C.; software, F.D. ; validation, F.D. ; formal analysis, F.D. ; investigation, F.D. ; resources, W.C.; data curation, W.C.; writing--original draft preparation, F.D. and W.C.; writing--review and editing, F.D.