5 papers · 1 filter
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)…
Unveiling High-Probability Generalization in Decentralized SGD
Jiahuan Wang, Ping Luo, Ziqing Wen +2
Decentralized stochastic gradient descent (D-SGD) is an efficient method for large-scale distributed learning. Existing generalization studies mainly address expected results, achi…
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…