Asynchronous Probability Ensembling for Federated Disaster Detection

ArXi:2604.14450v1 Announce Type: new Quick and accurate emergency handling in Disaster Decision Systems (DDSS) is often hampered by network latency and suboptimal application accuracy. While Federated Learning (FL) addresses some of these issues, it is constrained by high communication costs and rigid synchronization requirements across heterogeneous convolutional neural network (CNN) architectures. To overcome these challenges, this paper proposes a decentralized ensembling framework based on asynchronous probability aggregation and feedback distillation.