A decentralized personalized federated learning method that scores peers by loss, header similarity, and recency reports faster convergence, but its own CIFAR-100 result contradicts the accuracy claim.
Interpretable data fusion for distributed learning: A representative approach via gradient matching,
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
2025 1verdicts
REJECT 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
PFedDST: Personalized Federated Learning with Decentralized Selection Training
A decentralized personalized federated learning method that scores peers by loss, header similarity, and recency reports faster convergence, but its own CIFAR-100 result contradicts the accuracy claim.