Federated learning (FL) is a distributed learning framework that allows clients to build models without disclosing local data. However, in resource-constrained scenarios, it is costly to participate in FL for all clients. Hence, selection strategy should be designed to select the most appropriate client groups. Current selection strategies are mainly cost and accuracy oriented, ignoring the autonomy of clients, which leads to the inability of clients to make autonomous decisions when participating in model training and updating. To realize autonomous selection of clients, we design a novel model accuracy-aware bidirectional client selection (MABCS) algorithm. The MABCS algorithm implements selection from both server and client dimensions. Specifically, the server evaluates the contributions of clients and design an accuracy-aware dynamic incentive mechanism. The client measures participation autonomy based on the reward and cost to decide whether or not to participate in FL. Thus, the client selection problem is modeled as a joint nonconvex optimization problem that maximizes the system revenue by optimizing the selection strategy and resource allocation strategy. The block coordinate descent algorithm is utilized to decouple the selection strategy and resource allocation strategy, and a linear approximation is employed to transform the selection strategy problem into a convex problem. An alternating optimization algorithm is used for the subproblems after the decomposition to obtain a near-optimal solution. Simulation results indicate that the MABCS algorithm exhibits superior convergence performance compared with other benchmark schemes.

中文翻译：

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结合客户端自主性的联邦学习的双向选择：一种准确性感知激励方法


联合学习 （FL） 是一种分布式学习框架，允许客户在不泄露本地数据的情况下构建模型。但是，在资源受限的情况下，所有客户端参与 FL 的成本很高。因此，应设计选择策略以选择最合适的客户组。目前的选择策略主要以成本和精度为导向，忽视了客户端的自主性，导致客户端在参与模型训练和更新时无法自主决策。为了实现客户端的自主选择，我们设计了一种新的模型准确性感知双向客户端选择 （MABCS） 算法。MABCS 算法实现从服务器和客户端维度的选择。具体来说，服务器评估客户端的贡献并设计一个准确性感知的动态激励机制。客户根据奖励和成本来衡量参与自主权，以决定是否参与 FL。因此，将客户端选择问题建模为一个联合非凸优化问题，通过优化选择策略和资源分配策略来最大化系统收益。采用块坐标下降算法对选择策略和资源分配策略进行解耦，采用线性逼近将选择策略问题转化为凸问题。对分解后的子问题使用交替优化算法，以获得接近最优的解。仿真结果表明，与其他基准方案相比，MABCS 算法表现出优异的收敛性能。