9 citations · 24 across the 11 of their papers we have counts for
11 papers
FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation
Minghui Chen, Meirui Jiang, Qi Dou +2
Cross-silo federated learning (FL) enables the development of machine learning models on datasets distributed across data centers such as hospitals and clinical research laboratori…
Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical Imaging
Meirui Jiang, Yuan Zhong, Anjie Le +2
Despite recent progress in enhancing the privacy of federated learning (FL) via differential privacy (DP), the trade-off of DP between privacy protection and performance is still u…
Scalable Data Point Valuation in Decentralized Learning
Konstantin D. Pandl, Chun-Yin Huang, Ivan Beschastnikh +3
Existing research on data valuation in federated and swarm learning focuses on valuing client contributions and works best when data across clients is independent and identically d…
GMValuator: Similarity-based Data Valuation for Generative Models
Jiaxi Yang, Wenglong Deng, Benlin Liu +3
Data valuation plays a crucial role in machine learning. Existing data valuation methods, mainly focused on discriminative models, overlook generative models that have gained atten…
Federated Learning on Virtual Heterogeneous Data with Local-global Distillation
Chun-Yin Huang, Ruinan Jin, Can Zhao +2
While Federated Learning (FL) is gaining popularity for training machine learning models in a decentralized fashion, numerous challenges persist, such as asynchronization, computat…
Robust Split Federated Learning for U-shaped Medical Image Networks
Ziyuan Yang, Yingyu Chen, Huijie Huangfu +4
U-shaped networks are widely used in various medical image tasks, such as segmentation, restoration and reconstruction, but most of them usually rely on centralized learning and th…
A Convergence Theory for Federated Average: Beyond Smoothness
Xiaoxiao Li, Zhao Song, Runzhou Tao +1
Federated learning enables a large amount of edge computing devices to learn a model without data sharing jointly. As a leading algorithm in this setting, Federated Average FedAvg,…
Backdoor Attack and Defense in Federated Generative Adversarial Network-based Medical Image Synthesis
Ruinan Jin, Xiaoxiao Li
Deep Learning-based image synthesis techniques have been applied in healthcare research for generating medical images to support open research and augment medical datasets. Trainin…
FedMT: Federated Learning with Mixed-type Labels
Qiong Zhang, Jing Peng, Xin Zhang +3
In federated learning (FL), classifiers (e.g., deep networks) are trained on datasets from multiple data centers without exchanging data across them, which improves the sample effi…
Federated Adversarial Learning: A Framework with Convergence Analysis
Xiaoxiao Li, Zhao Song, Jiaming Yang
Federated learning (FL) is a trending training paradigm to utilize decentralized training data. FL allows clients to update model parameters locally for several epochs, then share…
UniFed: A Unified Framework for Federated Learning on Non-IID Image Features
Meirui Jiang, Xiaoxiao Li, Xiaofei Zhang +2
How to tackle non-iid data is a crucial topic in federated learning. This challenging problem not only affects training process, but also harms performance of clients not participa…