用基因型/表型映射优化联邦学习模型聚合，提升通信系统适应性。
FedAvgen: Metadata for Model Aggregation In Communication Systems
- 将模型权重视为基因型，以元数据建模表型，实现智能聚合
- 相比FedAvg与FedSGD，提升全局模型泛化性能
- 适合设备异构性强的通信系统场景
为提升业务效率并降低成本，人工智能从业者正从零开始训练模型转向共享预训练模型。这些模型被聚合为具备更强泛化能力的全局模型，并分发至客户端设备。该方法即联邦学习，其核心在于选择参与平均的客户端模型。在通信系统中，设备配置差异显著，带来挑战。为此，本文提出元启发式算法FedAvgen，将每个预训练模型与其权重空间分别对应为表型与基因型，模拟父代-子代遗传演化过程，刻画联邦学习中的全局平均步骤。实验对比了FedAvg和FedSGD两种主流基线算法，验证了本方法在模型性能上的优势。
原文摘要 · Abstract (English)
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).
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