Author name cluster

Ran Zhu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

AAAI Conference 2026 Conference Paper

SMoFi: Step-wise Momentum Fusion for Split Federated Learning on Heterogeneous Data

Mingkun Yang
Ran Zhu
Qing Wang
Jie Yang

Split Federated Learning is a system-efficient federated learning paradigm that leverages the rich computing resources at a central server to train model partitions. Data heterogeneity across silos, however, presents a major challenge undermining the convergence speed and accuracy of the global model. This paper introduces Step-wise Momentum Fusion (SMoFi), an effective and lightweight framework that counteracts gradient divergence arising from data heterogeneity by synchronizing the momentum buffers across server-side optimizers. To control gradient divergence over the training process, we design a staleness-aware alignment mechanism that imposes constraints on gradient updates of the server-side submodel at each optimization step. Extensive validations on multiple real-world datasets show that SMoFi consistently improves global model accuracy (up to 7.1%) and convergence speed (up to 10.25x). Furthermore, SMoFi has a greater impact with more clients involved and deeper learning models, making it particularly suitable for model training in resource-constrained contexts.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

Flick: Empowering Federated Learning with Commonsense Knowledge

Ran Zhu
Mingkun Yang
Shiqiang Wang
Jie Yang
Qing Wang

Federated Learning (FL) has emerged as a privacy-preserving framework for training models on data generated at the edge. However, the heterogeneity of data silos (e. g. , label skew and domain shift) often leads to inconsistent learning objectives and suboptimal model performance. Inspired by the data-driven approach, we propose Flick, a novel data generation framework for heterogeneous **F**ederated **L**earning w**i**th **C**ommonsense **K**nowledge from Large Language Models (LLMs). In Flick, the client performs the local data summary to capture client-specific knowledge in textual form. The central server then distills task-relevant, high-quality knowledge from the out-of-the-box LLM -- guided by cross-client-specific insights -- to generate informative text prompts. These prompts direct a generative model in producing synthetic data, enabling global model fine-tuning and local data compensation. This process gradually aligns the label and feature distributions across clients. Extensive results on three datasets demonstrate that Flick improves the global model accuracy by up to 11. 43\%, and accelerates convergence by up to 12. 9$\times$, validating its effectiveness in addressing data heterogeneity.

PDF Details

ICLR Conference 2024 Conference Paper

FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning

Mingkun Yang
Ran Zhu
Qing Wang 0007
Jie Yang 0028

Federated Learning (FL) is an important privacy-preserving learning paradigm that plays an important role in the Intelligent Internet of Things. Training a global model in FL, however, is vulnerable to the noise in the heterogeneous data across the clients. In this paper, we introduce **FedTrans**, a novel client-transparent client utility estimation method designed to guide client selection for noisy scenarios, mitigating performance degradation problems. To estimate the client utility, we propose a Bayesian framework that models client utility and its relationships with the weight parameters and the performance of local models. We then introduce a variational inference algorithm to effectively infer client utility, given only a small amount of auxiliary data. Our evaluation demonstrates that leveraging FedTrans as a guide for client selection can lead to a better accuracy performance (up to 7.8\%), ensuring robustness in noisy scenarios.

Details