Proposes FedACnnL for analytic layer-wise DNN training in federated settings and pFedACnnL for analytic personalized meta-learning, claiming 83-99% training time reduction and 4-8% accuracy gains over baselines with SOTA results in most tested cases.
Gradient free personalized federated learning, in: Proceedings of the 53rd International Conference on Parallel Processing, pp
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Analytic Personalized Federated Meta-Learning
Proposes FedACnnL for analytic layer-wise DNN training in federated settings and pFedACnnL for analytic personalized meta-learning, claiming 83-99% training time reduction and 4-8% accuracy gains over baselines with SOTA results in most tested cases.