10 papers
Solution-space heterogeneity shapes federated learning dynamics across partial differential equations
Ping Luo, Jiahuan Wang, Ziqing Wen +2
Federated scientific machine learning enables institutions to train neural surrogates without centralizing local physical data, yet studies of partial differential equations (PDEs)…
FedACT: Federated Adaptive Coordinate Trust Modulation for Robust Transformer Training under Data Heterogeneity
Shuai Li, Qinglin Wang, Ping Luo +8
Federated Transformer training increasingly relies on local AdamW, whose adaptive updates can provide much stronger local progress than SGD-based training. However, under heterogen…
MoLS: SNR-Guided Module-Wise Learning-Rate Calibration for Large Language Models
Ziqing Wen, Zhouyang Liu, Jiahuan Wang +5
Large language models (LLMs) contain functionally distinct modules with heterogeneous gradient statistics. Although Adam(W) adapts updates at the parameter level, selecting relativ…
Stability and Generalization for Decentralized Markov SGD
Jiahuan Wang, Ziqing Wen, Ping Luo +2
Stochastic gradient methods are central to large-scale learning, yet their generalization theory typically relies on independent sampling assumptions. In many practical application…
Local Gradient Regulation Stabilizes Federated Learning under Client Heterogeneity
Ping Luo, Jiahuan Wang, Ziqing Wen +2
Federated learning (FL) enables collaborative model training across distributed clients without sharing raw data, yet its stability is fundamentally challenged by statistical heter…
FOAM: Blocked State Folding for Memory-Efficient LLM Training
Ziqing Wen, Jiahuan Wang, Ping Luo +2
Large language models (LLMs) have demonstrated remarkable performance due to their large parameter counts and extensive training data. However, their scale leads to significant mem…
GWT: Scalable Optimizer State Compression for Large Language Model Training
Ziqing Wen, Ping Luo, Jiahuan Wang +4
Training large language models (LLMs) requires substantial memory, a significant fraction of which is consumed by the moment states maintained by adaptive optimizers such as Adam.…
Federated Prediction-Powered Inference from Decentralized Data
Ping Luo, Xiaoge Deng, Ziqing Wen +2
In various domains, the increasing application of machine learning allows researchers to access inexpensive predictive data, which can be utilized as auxiliary data for statistical…
Adaptive Momentum Benefits Score-based Generative Models
Ziqing Wen, Xiaoge Deng, Ping Luo +3
Score-based Generative Models (SGMs) have achieved remarkable success in generation tasks by establishing a diffusion process that gradually perturbs real data into Gaussian noise…
Accelerating Federated Learning by Selecting Beneficial Herd of Local Gradients
Ping Luo, Xiaoge Deng, Ziqing Wen +2
Federated Learning (FL) is a distributed machine learning framework in communication network systems. However, the systems' Non-Independent and Identically Distributed (Non-IID) da…