FedHCA$^2$: Towards Hetero-Client Federated Multi-Task Learning

arXiv:2311.13250v2 Announce Type: replace-cross
Abstract: Federated Learning (FL) enables joint training across distributed clients using their local data privately. Federated Multi-Task Learning (FMTL) builds on FL to handle multiple tasks, assuming model congruity that identical model architecture is deployed in each client. To relax this assumption and thus extend real-world applicability, we introduce a novel problem setting, Hetero-Client Federated Multi-Task Learning (HC-FMTL), to accommodate diverse task setups. The main challenge of HC-FMTL is the model incongruity issue that invalidates conventional …

abstract architecture arxiv client cs.cv cs.lg data distributed federated learning multiple multi-task learning tasks training type world