25 citations · 33 across the 3 of their papers we have counts for
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cs.LG2022
FedAUXfdp: Differentially Private One-Shot Federated Distillation
Haley Hoech, Roman Rischke, Karsten Müller +1
Federated learning suffers in the case of non-iid local datasets, i.e., when the distributions of the clients' data are heterogeneous. One promising approach to this challenge is t…
cs.LG2021★ 8 cited
ECQ: Explainability-Driven Quantization for Low-Bit and Sparse DNNs
Daniel Becking, Maximilian Dreyer, Wojciech Samek +2
The remarkable success of deep neural networks (DNNs) in various applications is accompanied by a significant increase in network parameters and arithmetic operations. Such increas…