DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning

arXiv:2403.08506v1 Announce Type: new
Abstract: Federated learning (FL) has emerged as a powerful paradigm for learning from decentralized data, and federated domain generalization further considers the test dataset (target domain) is absent from the decentralized training data (source domains). However, most existing FL methods assume that domain labels are provided during training, and their evaluation imposes explicit constraints on the number of domains, which must strictly match the number of clients. Because of the underutilization of numerous edge devices and …

abstract arxiv cs.ai cs.cv cs.lg data dataset decentralized decentralized data domain domains federated learning however labels multiple paradigm prompt prompt tuning test training training data type