1.7k citations · 2.9k across the 35 of their papers we have counts for
9 papers · 1 filter
FedCiR: Client-Invariant Representation Learning for Federated Non-IID Features
Zijian Li, Zehong Lin, Jiawei Shao +2
Federated learning (FL) is a distributed learning paradigm that maximizes the potential of data-driven models for edge devices without sharing their raw data. However, devices ofte…
Feature Matching Data Synthesis for Non-IID Federated Learning
Zijian Li, Yuchang Sun, Jiawei Shao +3
Federated learning (FL) has emerged as a privacy-preserving paradigm that trains neural networks on edge devices without collecting data at a central server. However, FL encounters…
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…
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…
Stochastic Coded Federated Learning with Convergence and Privacy Guarantees
Yuchang Sun, Jiawei Shao, Songze Li +2
Federated learning (FL) has attracted much attention as a privacy-preserving distributed machine learning framework, where many clients collaboratively train a machine learning mod…
Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity
Yuchang Sun, Jiawei Shao, Yuyi Mao +2
Federated edge learning (FEEL) has attracted much attention as a privacy-preserving paradigm to effectively incorporate the distributed data at the network edge for training deep l…
Communication-Computation Efficient Device-Edge Co-Inference via AutoML
Xinjie Zhang, Jiawei Shao, Yuyi Mao +1
Device-edge co-inference, which partitions a deep neural network between a resource-constrained mobile device and an edge server, recently emerges as a promising paradigm to suppor…