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.

翻译：全去中心化学习能够将学习资源和决策能力分布到多个用户设备或节点上，因其隐私保护和去中心化的特性而迅速普及。重要的是，这种学习过程的众包模式允许系统在部分节点受影响或断开连接时仍能继续运行。在灾难场景中，通信基础设施和集中式系统可能被中断或完全不可用，从而阻碍在这些环境中执行标准的集中式学习任务。因此，全去中心化学习在此情况下能发挥重要作用。然而，从集中式向点对点通信的转变引入了学习过程与节点间通信图拓扑结构之间的依赖关系。在灾难场景中，即使是点对点通信也容易受到突变的影响，例如设备电量耗尽或因位置原因与其他设备断开连接。在本研究中，我们探讨了灾难环境下点对点通信的各种中断对去中心化学习的影响。我们考察了当部分设备突然退出学习过程时，去中心化学习过程的鲁棒性。为此，我们分析了丢失持有数据（即潜在知识）的设备与丢失仅贡献图连接性（即无数据）的设备之间的差异。在Barabasi-Albert图拓扑结构下，训练数据以IID方式分布在各节点上，我们的研究结果表明，学习过程的准确性受连接性损失的影响大于数据损失的影响。尽管如此，网络仍相对稳健，学习过程能够达到较好的准确性水平。