59 citations · 74 across the 6 of their papers we have counts for
6 papers
FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks
Jaehee Jang, Heonseok Ha, Dahuin Jung +1
Personalized federated learning is aimed at allowing numerous clients to train personalized models while participating in collaborative training in a communication-efficient manner…
Scalable Smartphone Cluster for Deep Learning
Byunggook Na, Jaehee Jang, Seongsik Park +7
Various deep learning applications on smartphones have been rapidly rising, but training deep neural networks (DNNs) has too large computational burden to be executed on a single s…
Security and Privacy Issues in Deep Learning
Ho Bae, Jaehee Jang, Dahuin Jung +4
To promote secure and private artificial intelligence (SPAI), we review studies on the model security and data privacy of DNNs. Model security allows system to behave as intended w…
Energy-Efficient Inference Accelerator for Memory-Augmented Neural Networks on an FPGA
Seongsik Park, Jaehee Jang, Seijoon Kim +1
Memory-augmented neural networks (MANNs) are designed for question-answering tasks. It is difficult to run a MANN effectively on accelerators designed for other neural networks (NN…
Homomorphic Parameter Compression for Distributed Deep Learning Training
Jaehee Jang, Byungook Na, Sungroh Yoon
Distributed training of deep neural networks has received significant research interest, and its major approaches include implementations on multiple GPUs and clusters. Paralleliza…
DeepSpark: A Spark-Based Distributed Deep Learning Framework for Commodity Clusters
Hanjoo Kim, Jaehong Park, Jaehee Jang +1
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…