1 citations · 2 across the 4 of their papers we have counts for
4 papers
Learn How to Query from Unlabeled Data Streams in Federated Learning
Yuchang Sun, Xinran Li, Tao Lin +1
Federated learning (FL) enables collaborative learning among decentralized clients while safeguarding the privacy of their local data. Existing studies on FL typically assume offli…
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…
DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning
Wenqiang Sun, Sen Li, Yuchang Sun +1
Federated learning (FL) attempts to train a global model by aggregating local models from distributed devices under the coordination of a central server. However, the existence of…
Asynchronous Semi-Decentralized Federated Edge Learning for Heterogeneous Clients
Yuchang Sun, Jiawei Shao, Yuyi Mao +1
Federated edge learning (FEEL) has drawn much attention as a privacy-preserving distributed learning framework for mobile edge networks. In this work, we investigate a novel semi-d…