DMBA maintains attack success rates above 80% for all backdoors in a distributed multi-target FL setting where baselines drop below 50%.
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ParaRNN decouples RNN dynamics into interpretable additive components, enabling parallelization and nonparametric regression bounds while matching vanilla RNN performance on sequential tasks.
A unified F-statistic screening and weighted evaluation method prunes both unstructured and structured parameters in FNNs and CNNs, claiming order-of-magnitude size reduction with competitive accuracy on vision datasets.
citing papers explorer
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Act in Collusion: Distributed Multi-Target Backdoor Attacks in Federated Learning
DMBA maintains attack success rates above 80% for all backdoors in a distributed multi-target FL setting where baselines drop below 50%.
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ParaRNN: An Interpretable and Parallelizable Recurrent Neural Network for Time-Dependent Data
ParaRNN decouples RNN dynamics into interpretable additive components, enabling parallelization and nonparametric regression bounds while matching vanilla RNN performance on sequential tasks.
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Exploring Vision Neural Network Pruning via Screening Methodology
A unified F-statistic screening and weighted evaluation method prunes both unstructured and structured parameters in FNNs and CNNs, claiming order-of-magnitude size reduction with competitive accuracy on vision datasets.