FedA2L adapts per-layer learning rates from local weight-divergence and aggregation-stability signals, accelerating convergence in decentralized federated learning without extra communication.
Digital twin driven smart factories: real time physics based co-simulation using edge ai and federated learning.Scientific Reports, 15(1):43373, 2025
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
2026 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
FedA2L: Adaptive layer-wise learning rate adjustment in decentralized federated learning
FedA2L adapts per-layer learning rates from local weight-divergence and aggregation-stability signals, accelerating convergence in decentralized federated learning without extra communication.