  Motivated by the heterogeneous nature of devices participating in large-scale
Federated Learning (FL) optimization, we focus on an asynchronous server-less
FL solution empowered by blockchain technology. In contrast to mostly adopted
FL approaches, which assume synchronous operation, we advocate an asynchronous
method whereby model aggregation is done as clients submit their local updates.
The asynchronous setting fits well with the federated optimization idea in
practical large-scale settings with heterogeneous clients. Thus, it potentially
leads to higher efficiency in terms of communication overhead and idle periods.
To evaluate the learning completion delay of BC-enabled FL, we provide an
analytical model based on batch service queue theory. Furthermore, we provide
simulation results to assess the performance of both synchronous and
asynchronous mechanisms. Important aspects involved in the BC-enabled FL
optimization, such as the network size, link capacity, or user requirements,
are put together and analyzed. As our results show, the synchronous setting
leads to higher prediction accuracy than the asynchronous case. Nevertheless,
asynchronous federated optimization provides much lower latency in many cases,
thus becoming an appealing solution for FL when dealing with large datasets,
tough timing constraints (e.g., near-real-time applications), or highly varying
training data.
