167 citations · 235 across the 4 of their papers we have counts for
7 papers · 1 filter
Efficient and Private Federated Learning with Partially Trainable Networks
Hakim Sidahmed, Zheng Xu, Ankush Garg +2
Federated learning is used for decentralized training of machine learning models on a large number (millions) of edge mobile devices. It is challenging because mobile devices often…
A Field Guide to Federated Optimization
Jianyu Wang, Zachary Charles, Zheng Xu +50
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy prote…
Local-Global Knowledge Distillation in Heterogeneous Federated Learning with Non-IID Data
Dezhong Yao, Wanning Pan, Yutong Dai +5
Federated learning enables multiple clients to collaboratively learn a global model by periodically aggregating the clients' models without transferring the local data. However, du…
Local Adaptivity in Federated Learning: Convergence and Consistency
Jianyu Wang, Zheng Xu, Zachary Garrett +3
The federated learning (FL) framework trains a machine learning model using decentralized data stored at edge client devices by periodically aggregating locally trained models. Pop…
GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training
Chen Zhu, Renkun Ni, Zheng Xu +3
Innovations in neural architectures have fostered significant breakthroughs in language modeling and computer vision. Unfortunately, novel architectures often result in challenging…
Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer
Chen Zhu, Zheng Xu, Ali Shafahi +3
When large scale training data is available, one can obtain compact and accurate networks to be deployed in resource-constrained environments effectively through quantization and p…