FedDW regularizes each client's classifier-weight similarity matrix toward a globally aggregated soft-label matrix, improving non-IID federated accuracy on four benchmarks by about 3% over ten baselines.
Federated optimization in heterogeneous networks,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning
FedDW regularizes each client's classifier-weight similarity matrix toward a globally aggregated soft-label matrix, improving non-IID federated accuracy on four benchmarks by about 3% over ten baselines.