用代理模型优化联邦学习客户端选择，提速降耗更稳定
EvoCSFL: Surrogate-Assisted Evolutionary Client Selection for Efficient and Robust Federated Learning
- 构建代理模型预测选中客户端的综合表现
- 在多个数据集上收敛更快、能耗更低、鲁棒性更强
- 适合资源受限场景下的高效联邦学习应用
客户端数据与系统异构性导致随机选客户端时联邦学习难以兼顾收敛速度与鲁棒性。本文提出一种基于代理模型的客户端进化选择框架。首先采用典型选择策略生成候选集，设计融合模型性能、通信延迟与能耗的指标函数，将客户端选择建模为组合优化问题。随后利用候选选择及其指标值构建代理模型，以高效近似不同客户端子集的性能。采用进化算法在组合空间中搜索，并由代理模型引导，加速收敛。在MNIST、CIFAR10、CINIC10和TinyImageNet上的实验表明，该方法相比现有方法实现了更快的收敛速度、更低的能耗和更高的鲁棒性。
原文摘要 · Abstract (English)
The heterogeneity of client data and systems makes it difficult to achieve satisfactory convergence speed and robustness in federated learning with random client selection. To address this issue, this paper proposes a surrogate-assisted client evolutionary selection framework for federated learning. In this framework, some typical client selection strategies are first used to generate candidate sets, and a metric function that integrates model performance, communication latency, and energy consumption is developed to formulate the client selection problem as a combinatorial optimization one. Subsequently, a surrogate model is constructed using the candidate selections and metric to efficiently approximate the performance of selected client subsets. An evolutionary algorithm is employed to search the combinatorial space of client selections, guided by the surrogate model to accelerate convergence. Experiments on MNIST, CIFAR10, CINIC10, and TinyImageNet demonstrate that the proposed algorithm achieves faster convergence, lower energy consumption, and improved robustness compared to existing methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。