  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.
