Federated learning has shown enormous promise as a way of training ML models in distributed environments while reducing communication costs and protecting data privacy. However, the rise of complex cyber-physical systems, such as the Internet-of-Things, presents new challenges that are not met with traditional FL methods. Hierarchical Federated Learning extends the traditional FL process to enable more efficient model aggregation based on application needs or characteristics of the deployment environment (e.g., resource capabilities and/or network connectivity). It illustrates the benefits of balancing processing across the cloud-edge continuum. Hierarchical Federated Learning is likely to be a key enabler for a wide range of applications, such as smart farming and smart energy management, as it can improve performance and reduce costs, whilst also enabling FL workflows to be deployed in environments that are not well-suited to traditional FL. Model aggregation algorithms, software frameworks, and infrastructures will need to be designed and implemented to make such solutions accessible to researchers and engineers across a growing set of domains. H-FL also introduces a number of new challenges. For instance, there are implicit infrastructural challenges. There is also a trade-off between having generalised models and personalised models. If there exist geographical patterns for data (e.g., soil conditions in a smart farm likely are related to the geography of the region itself), then it is crucial that models used locally can consider their own locality in addition to a globally-learned model. H-FL will be crucial to future FL solutions as it can aggregate and distribute models at multiple levels to optimally serve the trade-off between locality dependence and global anomaly robustness.

翻译：联邦学习已在分布式环境中展现出巨大潜力，既能降低通信成本，又可保护数据隐私。然而，随着物联网等复杂信息物理系统的兴起，传统联邦学习方法面临新的挑战。分层联邦学习扩展了传统联邦学习流程，可根据应用需求或部署环境特征（如资源能力与/或网络连接状况）实现更高效的模型聚合。该方法展示了在云-边连续体上平衡处理能力的优势。分层联邦学习很可能成为智慧农业、智慧能源管理等广泛应用的关键推动力，因其能在提升性能、降低成本的同时，使联邦学习工作流程部署于不适于传统联邦学习的环境。模型聚合算法、软件框架及基础设施需被设计与实现，以使此类解决方案能被日益增多的领域研究人员和工程师所使用。分层联邦学习还引入了若干新挑战。例如，存在隐性的基础设施挑战。此外，通用化模型与个性化模型之间存在权衡。若数据存在地理模式（如智慧农场的土壤条件通常与区域地理特征相关），则本地使用的模型除全局学习模型外，必须能考虑自身地域特性。分层联邦学习对未来联邦学习解决方案至关重要，因为它能在多层级别聚合与分发模型，从而优化处理地域依赖性与全局异常鲁棒性之间的权衡。