FedSB combines client-level label smoothing with equal-sized per-client training budgets and reports state-of-the-art accuracy on three of four federated domain generalization benchmarks.
Learning to generate novel domains for domain generalization,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training
FedSB combines client-level label smoothing with equal-sized per-client training budgets and reports state-of-the-art accuracy on three of four federated domain generalization benchmarks.