With the development of the Internet of Things (IoT), Federated Learning (FL) is extensively employed in smart cities and industrial IoT, involving numerous heterogeneous devices with varying computational and storage capabilities. Traditional FL assumes that clients have enough resources to train a unified global model from the beginning to the end of training. However, it ignores the problem of uneven and real-time changes in client resources. Additionally, there are aggregation difficulties between heterogeneous client models and global model. To address these challenges, we propose an Adaptive Federated Learning Framework for Heterogeneous Devices (AdaptFL). In AdaptFL, we employ a resource-aware neural architecture search method, which searches for models based on each client’s resource conditions. It enables AdaptFL to automatically assign customized models tailored to each client’s specific resource conditions in the current round. Additionally, we employ a staged knowledge distillation strategy to facilitate efficient distribution and aggregation between the heterogeneous global model and the client models. Experimental results demonstrate that, compared to state-of-the-art model-level heterogeneous ablation methods, AdaptFL improves global test accuracy by 4% to 15% on the SVHN dataset and enhances accuracy by 5% to 14% in scenarios with heterogeneous data. Additionally, AdaptFL effectively reduces communication overhead by over 50% across all datasets. Furthermore, it offers a degree of resilience against model poisoning attacks.

中文翻译：

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AdaptFL：适用于异构设备的自适应联合学习框架


随着物联网 （IoT） 的发展，联邦学习 （FL） 被广泛用于智慧城市和工业物联网，涉及众多具有不同计算和存储能力的异构设备。传统 FL 假设客户端有足够的资源从训练开始到结束训练统一的全局模型。但是，它忽略了客户端资源不均匀和实时变化的问题。此外，异构客户端模型和全局模型之间存在聚合困难。为了应对这些挑战，我们提出了一种异构设备的自适应联合学习框架 （AdaptFL）。在 AdaptFL 中，我们采用了一种资源感知的神经架构搜索方法，该方法根据每个客户的资源条件搜索模型。它使 AdaptFL 能够自动分配针对当前轮中每个客户的特定资源条件量身定制的定制模型。此外，我们采用分阶段知识蒸馏策略来促进异构全局模型和客户模型之间的有效分发和聚合。实验结果表明，与最先进的模型级异构消融方法相比，AdaptFL 在 SVHN 数据集上将全局测试精度提高了 4% 到 15%，在具有异构数据的场景中将精度提高了 5% 到 14%。此外，AdaptFL 有效地将所有数据集的通信开销降低了 50% 以上。此外，它还提供了一定程度的弹性来抵御模型中毒攻击。