fair resource allocation in federated learning
时间: 2023-05-03 10:02:24 浏览: 76
公平分配资源在联邦学习中的应用是指在联邦学习过程中,要合理分配参与方的计算、通信和存储资源,使每个参与方都能享有相同的资源待遇和机会,从而实现公平。这需要通过一系列策略和算法来实现,如动态资源管理、任务分配平衡和收益机制设计等。同时,公平分配资源还需要考虑到数据隐私和安全问题,确保每个参与方的数据都能得到保护。
相关问题
查询以下文献的GB/T 7713.1-2006的标准格式,包含期、卷和起止页码: MA Z, CHEN X, MA T, et al. Deep Deterministic Policy Gradient Based Resource Allocation in Internet of Vehicles[C].PAAP 2020: Parallel Architectures, Algorithms and Programming ,2020,pp. 295–306.
Ma, Z., Chen, X., Ma, T., et al. (2020). Deep Deterministic Policy Gradient Based Resource Allocation in Internet of Vehicles. In Proceedings of PAAP 2020: Parallel Architectures, Algorithms and Programming, (pp. 295-306). (GB/T 7713.1-2006 标准格式)
Abstract—In heterogeneous networks (HetNets), user association approaches should be able to achieve load balancing among base stations (BSs). This paper investigates the joint optimization of user association and resource allocation in Backhaul-constrained HetNets for capacity enhancements. We consider two major limitations in HetNets: the backhaul bottleneck of BSs and the capability of user equipment (UE). We establish a framework based on a multi-leader multi-follower Stackelberg game, in which resource allocation is formulated as a follower-level game and user association is cast as a leader-level game. Because of the backhaul bottleneck of small BSs, the given preference order of users renders the final association result unstable. Thus, the resident-oriented GaleShapley (GS) algorithm is included in the proposed framework to obtain a stable single-BS association. Furthermore, congestion factors are introduced to reflect the relative backhaul congestion degrees of BSs, which enables load balancing among the small BSs in the proposed algorithm. The study considers user association and resource allocation with and without limitations on the number of serving users for small BSs in HetNets. Extensive simulation results suggest that the proposed algorithm can adaptively respond to a wide variety of network situations.中文
在异构网络(HetNets)中,用户关联方法应该能够实现基站(BS)之间的负载平衡。本文研究了在受限于回程链路的HetNets中,用户关联和资源分配的联合优化问题,以增强系统容量。我们考虑了HetNets中的两个主要限制:BS的回程瓶颈和用户设备(UE)的能力。我们建立了一个基于多领导者-多追随者Stackelberg博弈的框架,其中资源分配被形式化为追随者级别的博弈,用户关联被视为领导者级别的博弈。由于小型BS的回程瓶颈,给定的用户优先顺序导致最终关联结果不稳定。因此,该提出的框架中包括了面向居民的Gale-Shapley(GS)算法,以获得稳定的单BS关联。此外,引入了拥塞因子来反映BS的相对回程拥塞程度,从而实现了提出算法中小型BS之间的负载平衡。本研究考虑了HetNets中限制小型BS服务用户数量和不限制的用户关联和资源分配。广泛的仿真结果表明,该提出的算法能够适应各种网络情况。