Federated learning (FL) has become a promising answer to facilitating privacy-preserving collaborative learning in distributed IoT devices. However, device heterogeneity is a key challenge because IoT networks include devices with very different computational powers, memory availability, and network environments. To this end, we introduce ASA (Adaptive Smart Agent). This new framework clusters devices adaptively based on real-time resource profiles and adapts customized models suited to every cluster's capability. ASA capitalizes on an intelligent agent layer that examines CPU power, available memory, and network environment to categorize devices into three levels: high-performance, mid-tier, and low-capability. Each level is provided with a model tuned to its computational power to ensure inclusive engagement across the network. Experimental evaluation on two benchmark datasets, MNIST and CIFAR-10, proves that ASA decreases communication burden by 43% to 50%, improves resource utilization by 43%, and achieves final model accuracies of 98.89% on MNIST and 85.30% on CIFAR-10. These results highlight ASA's efficacy in enhancing efficiency, scalability, and fairness in heterogeneous FL environments, rendering it a suitable answer for real-world IoT apps.

翻译：联邦学习已成为在分布式物联网设备中促进隐私保护协同学习的一种有前景的解决方案。然而，设备异构性是一个关键挑战，因为物联网网络包含计算能力、内存可用性和网络环境差异巨大的设备。为此，我们提出了ASA（自适应智能体）。这一新框架基于实时资源画像自适应地对设备进行聚类，并为每个能力集群适配定制化模型。ASA利用一个智能体层，通过检查CPU算力、可用内存和网络环境，将设备划分为三个层级：高性能、中端和低能力。每个层级都获得一个根据其计算能力调整的模型，以确保网络内的包容性参与。在MNIST和CIFAR-10两个基准数据集上的实验评估表明，ASA将通信负担降低了43%至50%，将资源利用率提高了43%，并在MNIST和CIFAR-10上分别实现了98.89%和85.30%的最终模型准确率。这些结果凸显了ASA在异构联邦学习环境中提升效率、可扩展性和公平性方面的效能，使其成为现实世界物联网应用的一个合适解决方案。