62 citations · 170 across the 44 of their papers we have counts for
6 papers · 1 filter
Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity
Yiyue Chen, Haris Vikalo, Chianing Wang
Motivated by high resource costs of centralized machine learning schemes as well as data privacy concerns, federated learning (FL) emerged as an efficient alternative that relies o…
Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices
Huancheng Chen, Haris Vikalo
While federated learning (FL) systems often utilize quantization to battle communication and computational bottlenecks, they have heretofore been limited to deploying fixed-precisi…
Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning
Huancheng Chen, Haris Vikalo
Statistical heterogeneity of data present at client devices in a federated learning (FL) system renders the training of a global model in such systems difficult. Particularly chall…
XVir: A Transformer-Based Architecture for Identifying Viral Reads from Cancer Samples
Shorya Consul, John Robertson, Haris Vikalo
It is estimated that approximately 15% of cancers worldwide can be linked to viral infections. The viruses that can cause or increase the risk of cancer include human papillomaviru…
Automotive RADAR sub-sampling via object detection networks: Leveraging prior signal information
Madhumitha Sakthi, Ahmed Tewfik, Marius Arvinte +1
Automotive radar has increasingly attracted attention due to growing interest in autonomous driving technologies. Acquiring situational awareness using multimodal data collected at…
The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation
Huancheng Chen, Johnny, Wang +1
Heterogeneity of data distributed across clients limits the performance of global models trained through federated learning, especially in the settings with highly imbalanced class…