FLASH: Federated Learning Across Simultaneous Heterogeneities

arXiv:2402.08769v1 Announce Type: new
Abstract: The key premise of federated learning (FL) is to train ML models across a diverse set of data-owners (clients), without exchanging local data. An overarching challenge to this date is client heterogeneity, which may arise not only from variations in data distribution, but also in data quality, as well as compute/communication latency. An integrated view of these diverse and concurrent sources of heterogeneity is critical; for instance, low-latency clients may have poor data quality, and …

abstract arxiv challenge client cs.dc cs.lg data data quality distribution diverse federated learning flash key ml models quality set the key train type