FedSFR lets poorly connected clients upload compact encoder features, which the server uses in a feature-reconstruction step, improving the stability and efficiency of federated training for vector-quantized image semantic communication.
Federated learning-based co- operative model training for task-oriented semantic communication,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
contradiction 1
citation-polarity summary
fields
eess.SP 1years
2025 1verdicts
CONDITIONAL 1roles
contradiction 1polarities
contest 1representative citing papers
citing papers explorer
-
Federated Learning Enhanced by Feature Reconstruction for Semantic Communication Module Updates of Agents
FedSFR lets poorly connected clients upload compact encoder features, which the server uses in a feature-reconstruction step, improving the stability and efficiency of federated training for vector-quantized image semantic communication.