Abstract
To improve business efficiency and minimize costs, Artificial Intelligence (AI) practitioners have adopted a shift from formulating models from scratch towards sharing pretrained models. The pretrained models are then aggregated into a global model with higher generalization capabilities, which is afterwards distributed to the client devices. This approach is known as federated learning and inherently utilizes different techniques to select the candidate client models averaged to obtain the global model. This approach, in the case of communication systems, faces challenges arising from the existential diversity in device profiles. The multiplicity in profiles motivates our conceptual assessment of a metaheuristic algorithm (FedAvgen), which relates each pretrained model with its weight space as metadata, to a phenotype and genotype, respectively. This parent-child genetic evolution characterizes the global averaging step in federated learning. We then compare the results of our approach to two widely adopted baseline federated learning algorithms like Federated Averaging (FedAvg) and Federated Stochastic Gradient Descent (FedSGD).
Abstract (translated)
为了提高业务效率并减少成本，人工智能（AI）从业者已从零开始构建模型转向共享预训练模型。这些预训练模型会被聚合为一个具有更强泛化能力的全局模型，然后分发到客户端设备上。这种方法被称为联邦学习，并且内在地利用不同的技术来选择用于获得全局模型的候选客户端模型的平均值。在通信系统中，该方法面临着由于设备配置文件存在多样性而产生的挑战。这种配置文件的多样化促使我们对一种元启发式算法（FedAvgen）进行概念评估，该算法将每个预训练模型与其权重空间分别视为元数据中的表型和基因型。这种父代-子代遗传进化特征化了联邦学习中的全局平均步骤。然后，我们将我们的方法的结果与两个广泛采用的基线联邦学习算法，如联邦平均（FedAvg）和联邦随机梯度下降（FedSGD），进行比较。
URL
https://arxiv.org/abs/2505.05486
