7 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…
How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning
Yuchang Sun, Marios Kountouris, Jun Zhang
Federated learning (FL) has attracted vivid attention as a privacy-preserving distributed learning framework. In this work, we focus on cross-silo FL, where clients become the mode…
Exploring Selective Layer Fine-Tuning in Federated Learning
Yuchang Sun, Yuexiang Xie, Bolin Ding +2
Federated learning (FL) has emerged as a promising paradigm for fine-tuning foundation models using distributed data in a privacy-preserving manner. Under limited computational res…
Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data
Yuchang Sun, Jiawei Shao, Yuyi Mao +2
Federated edge learning (FEEL) has emerged as an effective approach to reduce the large communication latency in Cloud-based machine learning solutions, while preserving data priva…
Dual-Delayed Asynchronous SGD for Arbitrarily Heterogeneous Data
Xiaolu Wang, Yuchang Sun, Hoi-To Wai +1
We consider the distributed learning problem with data dispersed across multiple workers under the orchestration of a central server. Asynchronous Stochastic Gradient Descent (SGD)…
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…