摘要：

在无蜂窝网络环境下实施联邦学习(FL)时, 用户调度和资源分配策略对优化系统时间开销、提升用户可达速率以及加速FL收敛速率至关重要。为解决资源分配不均的问题, 设计一种联合用户调度、CPU处理频率和功率分配的优化方案。通过最大化系统的最小用户速率来实现资源的公平分配, 并提升FL性能。联合优化问题被分解为用户调度和功率分配两个子问题。在用户调度方面, 设计基于k-means聚类的贪婪调度算法, 以综合评估用户的信道状态和数据"价值", 并将用户划分为不同的群组。随后, 针对每个群组的资源占用情况, 为组内用户制定个性化的CPU处理频率分配方案。最后, 通过在各群组中独立执行用户调度, 实现高效且精准的用户选择, 并通过提前分组有效降低用户选择的复杂度。在功率分配方面, 引入基于二分法的功率分配算法(BM-PA)。该算法不仅考虑了用户间的公平性, 还针对资源受限用户进行了优先处理, 以确保其能够获得更优质的资源分配。BM-PA算法通过低复杂度的迭代优化过程, 实现了功率分配的快速收敛, 并在保证系统性能的同时, 显著提升了资源的利用效率。合理的用户调度策略是功率分配子问题获得最优解的基础, 采用交替迭代的方法允许在每个子问题中独立进行优化, 同时考虑到另一个子问题的解。这种相互依赖的关系通过多轮迭代优化过程, 确保功率资源被合理地分配给那些最需要或最有可能有效利用它们的用户, 从而使系统整体性能得到提升, 实现联合优化求解, 显著提升系统整体性能。仿真实验结果表明, 与基准算法相比, 所提算法在下行可达速率方面, 最佳平均提升幅度高达103.34%, 在上行可达速率方面, 最佳提升幅度达到102.78%。此外, 相较于基准算法还能平均节省67.44%的FL任务训练时间, 特别是在FL学习模型精度达到90%时, 所提算法的时间开销最小。

关键词: 无蜂窝网络, 联邦学习, 用户调度, k-means聚类, 资源优化

Abstract:

When implementing Federated Learning (FL) in a cell-free network environment, user scheduling and resource allocation strategies are crucial for optimizing system time overhead, improving user reachability, and accelerating FL convergence rate. To address the issue of uneven resource allocation, this study designs an optimization scheme that combines user scheduling, CPU processing frequency, and power allocation. This scheme aims to achieve fair resource allocation by maximizing the minimum user rate in the system, thus enhancing FL performance. The joint optimization problem is decomposed into two subproblems: user scheduling and power allocation. For user scheduling, this study proposes a greedy scheduling algorithm based on k-means clustering to comprehensively evaluate channel conditions and data "value" of users and categorize users into different groups. Subsequently, for the resource occupation situation, a personalized CPU processing frequency allocation plan is developed for users within each group based on their resource occupancy. Finally, by independently executing user scheduling within each group, user selection is performed efficiently and precisely, and the complexity of user selection is effectively reduced via early grouping. For power allocation, this study introduces a Bisection Method-based Power Allocation (BM-PA) algorithm. This algorithm not only considers fairness among users but also prioritizes resource-constrained users to ensure that they can obtain superior resource allocation. The BM-PA algorithm achieves fast convergence of power allocation using a low-complexity iterative optimization process, significantly improving the resource utilization efficiency without deteriorating the system performance. In this study, a reasonable user scheduling strategy serves as the foundation for obtaining optimal solutions for the power allocation subproblem. This study adopts an alternating iteration method that allows independent optimization in each subproblem while considering the solution of the other subproblem. Via multiple rounds of iterative optimization, this interdependent relationship ensures that power resources are reasonably allocated to users who need them the most or are most likely to effectively utilize them, thus enhancing the overall system performance. This study realizes joint optimization solutions that significantly improve overall system performance. Simulation results show that compared with the baseline algorithm, the proposed algorithm exhibits outstanding performance in terms of downlink achievable rates-the average improvement reaches up to 103.34% under optimal conditions. Additionally, the uplink achievable rates improve by up to 102.78%. Furthermore, the proposed algorithm can save 67.44% of the FL task training time on average compared to the baseline algorithm, particularly when the FL learning model accuracy reaches 90%, wherein the time overhead of the proposed algorithm is minimal.

Key words: cell-free network, Federated Learning (FL), user scheduling, k-means clustering, resource optimization