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Yipeng Li

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5 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

A Unified Analysis of Stochastic Gradient Descent with Arbitrary Data Permutations and Beyond

Yipeng Li
Xinchen Lyu
Zhenyu Liu

We aim to provide a unified convergence analysis for permutation-based Stochastic Gradient Descent (SGD), where data examples are permuted before each epoch. By examining the relations among permutations, we categorize existing permutation-based SGD algorithms into three categories: Arbitrary Permutations, Independent Permutations (including Random Reshuffling and FlipFlop Rajput et al. , 2022), Dependent Permutations (including GraBs Lu et al. , 2022a; Cooper et al. , 2023). Existing unified analyses failed to encompass the Dependent Permutations category due to the inter-epoch permutation dependency. In this work, we propose a generalized assumption that explicitly characterizes the dependence of permutations across epochs. Building upon this assumption, we develop a unified framework for permutation-based SGD with arbitrary permutations of examples, incorporating all the existing permutation-based SGD algorithms. Furthermore, we adapt our framework for Federated Learning (FL), developing a unified framework for regularized client participation FL with arbitrary permutations of clients.

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JMLR Journal 2025 Journal Article

Sharp Bounds for Sequential Federated Learning on Heterogeneous Data

Yipeng Li
Xinchen Lyu

There are two paradigms in Federated Learning (FL): parallel FL (PFL), where models are trained in a parallel manner across clients, and sequential FL (SFL), where models are trained in a sequential manner across clients. Specifically, in PFL, clients perform local updates independently and send the updated model parameters to a global server for aggregation; in SFL, one client starts its local updates only after receiving the model parameters from the previous client in the sequence. In contrast to that of PFL, the convergence theory of SFL on heterogeneous data is still lacking. To resolve the theoretical dilemma of SFL, we establish sharp convergence guarantees for SFL on heterogeneous data with both upper and lower bounds. Specifically, we derive the upper bounds for the strongly convex, general convex and non-convex objective functions, and construct the matching lower bounds for the strongly convex and general convex objective functions. Then, we compare the upper bounds of SFL with those of PFL, showing that SFL outperforms PFL on heterogeneous data (at least, when the level of heterogeneity is relatively high). Experimental results validate the counterintuitive theoretical finding. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2025. ( edit, beta )

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NeurIPS Conference 2023 Conference Paper

Convergence Analysis of Sequential Federated Learning on Heterogeneous Data

Yipeng Li
Xinchen Lyu

There are two categories of methods in Federated Learning (FL) for joint training across multiple clients: i) parallel FL (PFL), where clients train models in a parallel manner; and ii) sequential FL (SFL), where clients train models in a sequential manner. In contrast to that of PFL, the convergence theory of SFL on heterogeneous data is still lacking. In this paper, we establish the convergence guarantees of SFL for strongly/general/non-convex objectives on heterogeneous data. The convergence guarantees of SFL are better than that of PFL on heterogeneous data with both full and partial client participation. Experimental results validate the counterintuitive analysis result that SFL outperforms PFL on extremely heterogeneous data in cross-device settings.

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AAAI Conference 2020 Conference Paper

Attention-Based Multi-Modal Fusion Network for Semantic Scene Completion

Siqi Li
Changqing Zou
Yipeng Li
Xibin Zhao
Yue Gao

This paper presents an end-to-end 3D convolutional network named attention-based multi-modal fusion network (AMFNet) for the semantic scene completion (SSC) task of inferring the occupancy and semantic labels of a volumetric 3D scene from single-view RGB-D images. Compared with previous methods which use only the semantic features extracted from RGB-D images, the proposed AMFNet learns to perform effective 3D scene completion and semantic segmentation simultaneously via leveraging the experience of inferring 2D semantic segmentation from RGB-D images as well as the reliable depth cues in spatial dimension. It is achieved by employing a multi-modal fusion architecture boosted from 2D semantic segmentation and a 3D semantic completion network empowered by residual attention blocks. We validate our method on both the synthetic SUNCG-RGBD dataset and the real NYUv2 dataset and the results show that our method respectively achieves the gains of 2. 5% and 2. 6% on the synthetic SUNCG-RGBD dataset and the real NYUv2 dataset against the state-of-the-art method.

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ICRA Conference 2014 Conference Paper

Dynamic visual localization and tracking method based on RGB-D information

Chunxia Yin
Cai Luo
Yipeng Li
Qionghai Dai

This paper proposes a new dynamic visual localization and tracking method using RGB-D camera. The proposed method makes use of band-width matrix, combines particle filter and mean shift with a new strategy, avoids falling into local optimum, and maintains particle diversity with only a few sampled points. A fast object searching strategy is successfully used to find the missing object. Experiments show that the proposed method runs robustly in complex scenes. The object 3-D parameters relative to the camera center can be estimated with a RGB-D camera, and that makes significant sense in spatial localization.

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