61 citations · 326 across the 44 of their papers we have counts for
6 papers · 1 filter
MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates
Yuchang Sun, Yuyi Mao, Jun Zhang
Federated learning (FL) is a promising framework for privacy-preserving collaborative learning, where model training tasks are distributed to clients and only the model updates nee…
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning
Yuchang Sun, Zehong lin, Yuyi Mao +2
Federated learning (FL) is a popular privacy-preserving distributed training scheme, where multiple devices collaborate to train machine learning models by uploading local model up…
Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Data
Tailin Zhou, Zehong Lin, Jun Zhang +1
Model averaging is a widely adopted technique in federated learning (FL) that aggregates multiple client models to obtain a global model. Remarkably, model averaging in FL yields a…
CAB: Comprehensive Attention Benchmarking on Long Sequence Modeling
Jun Zhang, Shuyang Jiang, Jiangtao Feng +2
Transformer has achieved remarkable success in language, image, and speech processing. Recently, various efficient attention architectures have been proposed to improve transformer…
Resource-Constrained Edge AI with Early Exit Prediction
Rongkang Dong, Yuyi Mao, Jun Zhang
By leveraging the data sample diversity, the early-exit network recently emerges as a prominent neural network architecture to accelerate the deep learning inference process. Howev…
Federated Learning with GAN-based Data Synthesis for Non-IID Clients
Zijian Li, Jiawei Shao, Yuyi Mao +2
Federated learning (FL) has recently emerged as a popular privacy-preserving collaborative learning paradigm. However, it suffers from the non-independent and identically distribut…