The number of devices, from smartphones to IoT hardware, interconnected via the Internet is growing
all the time. These devices produce a large amount of data that cannot be analyzed in any data
center or stored in the cloud, and it might be private or sensitive, thus precluding existing classic
approaches. However, studying these data and gaining insights from them is still of great relevance
to science and society. Recently, two related paradigms try to address the above problems. On the
one hand, edge computing (EC) suggests to increase processing on edge devices. On the other hand,
federated learning (FL) deals with training a shared machine learning (ML) model in a distributed
(non-centralized) manner while keeping private data locally on edge devices. The combination of
both is known as federated edge learning (FEEL). In this work, we propose an algorithm for FEEL
that adapts to asynchronous clients joining and leaving the computation. Our research focuses on
adapting the learning when the number of volunteers is low and may even drop to zero. We propose,
implement, and evaluate a new software platform for this purpose. We then evaluate its results
on problems relevant to FEEL. The proposed decentralized and adaptive system architecture for
asynchronous learning allows volunteer users to yield their device resources and local data to train a
shared ML model. The platform dynamically self-adapts to variations in the number of collaborating
heterogeneous devices due to unexpected disconnections (i.e., volunteers can join and leave at any
time). Thus, we conduct comprehensive empirical analysis in a static configuration and highly dynamic
and changing scenarios. The public open-source platform enables interoperability between volunteers
connected using web browsers and Python processes. (...)
