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Found 6 papers, 1 papers with code
FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks
While the existing methods require the collection of auxiliary data or model weights to generate a counterpart, FedClassAvg only requires clients to communicate with a couple of fully connected layers, which is highly communication-efficient.
Scalable Smartphone Cluster for Deep Learning
We implemented large-batch synchronous training of DNNs based on Caffe, a deep learning library.
Security and Privacy Issues in Deep Learning
Furthermore, the privacy of the data involved in model training is also threatened by attacks such as the model-inversion attack, or by dishonest service providers of AI applications.
Energy-Efficient Inference Accelerator for Memory-Augmented Neural Networks on an FPGA
Memory-augmented neural networks (MANNs) are designed for question-answering tasks.
Homomorphic Parameter Compression for Distributed Deep Learning Training
Distributed training of deep neural networks has received significant research interest, and its major approaches include implementations on multiple GPUs and clusters.
DeepSpark: A Spark-Based Distributed Deep Learning Framework for Commodity Clusters
The increasing complexity of deep neural networks (DNNs) has made it challenging to exploit existing large-scale data processing pipelines for handling massive data and parameters involved in DNN training.
