  Fully decentralized learning enables the distribution of learning resources
and decision-making capabilities across multiple user devices or nodes, and is
rapidly gaining popularity due to its privacy-preserving and decentralized
nature. Importantly, this crowdsourcing of the learning process allows the
system to continue functioning even if some nodes are affected or disconnected.
In a disaster scenario, communication infrastructure and centralized systems
may be disrupted or completely unavailable, hindering the possibility of
carrying out standard centralized learning tasks in these settings. Thus, fully
decentralized learning can help in this case. However, transitioning from
centralized to peer-to-peer communications introduces a dependency between the
learning process and the topology of the communication graph among nodes. In a
disaster scenario, even peer-to-peer communications are susceptible to abrupt
changes, such as devices running out of battery or getting disconnected from
others due to their position. In this study, we investigate the effects of
various disruptions to peer-to-peer communications on decentralized learning in
a disaster setting. We examine the resilience of a decentralized learning
process when a subset of devices drop from the process abruptly. To this end,
we analyze the difference between losing devices holding data, i.e., potential
knowledge, vs. devices contributing only to the graph connectivity, i.e., with
no data. Our findings on a Barabasi-Albert graph topology, where training data
is distributed across nodes in an IID fashion, indicate that the accuracy of
the learning process is more affected by a loss of connectivity than by a loss
of data. Nevertheless, the network remains relatively robust, and the learning
process can achieve a good level of accuracy.
