Chi Li

AAAI Conference 2025 Conference Paper

Federated Unlearning with Gradient Descent and Conflict Mitigation

• Zibin Pan
• Zhichao Wang
• Chi Li
• Kaiyan Zheng
• Boqi Wang
• Xiaoying Tang
• Junhua Zhao

Federated Learning (FL) has received much attention in recent years. However, although clients are not required to share their data in FL, the global model itself can implicitly remember clients' local data. Therefore, it’s necessary to effectively remove the target client's data from the FL global model to ease the risk of privacy leakage and implement "the right to be forgotten". Federated Unlearning (FU) has been considered a promising solution to remove data without full retraining. But the model utility easily suffers significant reduction during unlearning due to the gradient conflicts. Furthermore, when conducting the post-training to recovery the model utility, it’s prone to move back and revert what have already been unlearned. To address these issues, we propose Federated Unlearning with Orthogonal Steepest Descent (FedOSD). We first design an unlearning cross entropy loss to overcome the convergence issue of the gradient ascent. A steepest descent direction for unlearning is then calculated in the condition of being non-conflicting with other clients’ gradients and closest to the target client's gradient. This benefits to efficiently unlearn and mitigate the model utility reduction. After unlearning, we recover the model utility by maintaining the achievement of unlearning. Finally, extensive experiments in several FL scenarios verify that FedOSD outperforms the SOTA FU algorithms in terms of unlearning and the model utility.

AAAI Conference 2024 Conference Paper

FedLF: Layer-Wise Fair Federated Learning

• Zibin Pan
• Chi Li
• Fangchen Yu
• Shuyi Wang
• Haijin Wang
• Xiaoying Tang
• Junhua Zhao

Fairness has become an important concern in Federated Learning (FL). An unfair model that performs well for some clients while performing poorly for others can reduce the willingness of clients to participate. In this work, we identify a direct cause of unfairness in FL - the use of an unfair direction to update the global model, which favors some clients while conflicting with other clients’ gradients at the model and layer levels. To address these issues, we propose a layer-wise fair Federated Learning algorithm (FedLF). Firstly, we formulate a multi-objective optimization problem with an effective fair-driven objective for FL. A layer-wise fair direction is then calculated to mitigate the model and layer-level gradient conflicts and reduce the improvement bias. We further provide the theoretical analysis on how FedLF can improve fairness and guarantee convergence. Extensive experiments on different learning tasks and models demonstrate that FedLF outperforms the SOTA FL algorithms in terms of accuracy and fairness. The source code is available at https://github.com/zibinpan/FedLF.

AAAI Conference 2023 Conference Paper

FedMDFG: Federated Learning with Multi-Gradient Descent and Fair Guidance

• Zibin Pan
• Shuyi Wang
• Chi Li
• Haijin Wang
• Xiaoying Tang
• Junhua Zhao

Fairness has been considered as a critical problem in federated learning (FL). In this work, we analyze two direct causes of unfairness in FL - an unfair direction and an improper step size when updating the model. To solve these issues, we introduce an effective way to measure fairness of the model through the cosine similarity, and then propose a federated multiple gradient descent algorithm with fair guidance (FedMDFG) to drive the model fairer. We first convert FL into a multi-objective optimization problem (MOP) and design an advanced multiple gradient descent algorithm to calculate a fair descent direction by adding a fair-driven objective to MOP. A low-communication-cost line search strategy is then designed to find a better step size for the model update. We further show the theoretical analysis on how it can enhance fairness and guarantee the convergence. Finally, extensive experiments in several FL scenarios verify that FedMDFG is robust and outperforms the SOTA FL algorithms in convergence and fairness. The source code is available at https://github.com/zibinpan/FedMDFG.

YNICL Journal 2022 Journal Article

A multi-regression approach to improve optical coherence tomography diagnostic accuracy in multiple sclerosis patients without previous optic neuritis

• Jacqueline Chua
• Mihai Bostan
• Chi Li
• Yin Ci Sim
• Inna Bujor
• Damon Wong
• Bingyao Tan
• Xinwen Yao

ICRA Conference 2016 Conference Paper

Hierarchical semantic parsing for object pose estimation in densely cluttered scenes

• Chi Li
• Jonathan Bohren
• Eric Carlson
• Gregory D. Hager

Densely cluttered scenes are composed of multiple objects which are in close contact and heavily occlude each other. Few existing 3D object recognition systems are capable of accurately predicting object poses in such scenarios. This is mainly due to the presence of objects with textureless surfaces, similar appearances and the difficulty of object instance segmentation. In this paper, we present a hierarchical semantic segmentation algorithm which partitions a densely cluttered scene into different object regions. A RANSAC-based registration method is subsequently applied to estimate 6-DoF object poses within each object class. Part of this algorithm includes a generalized pooling scheme used to construct robust and discriminative object representations from a convolutional architecture with multiple pooling domains. We also provide a new RGB-D dataset which serves as a benchmark for object pose estimation in densely cluttered scenes. This dataset contains five thousand scene frames and over twenty thousand labeled poses of ten common hand tools. We show that our method demonstrates improved performance of pose estimation on this new dataset compared with other state-of-the-art methods.

IROS Conference 2016 Conference Paper

Incremental scene understanding on dense SLAM

• Chi Li
• Han Xiao
• Keisuke Tateno
• Federico Tombari
• Nassir Navab
• Gregory D. Hager

We present an architecture for online, incremental scene modeling which combines a SLAM-based scene understanding framework with semantic segmentation and object pose estimation. The core of this approach comprises a probabilistic inference scheme that predicts semantic labels for object hypotheses at each new frame. From these hypotheses, recognized scene structures are incrementally constructed and tracked. Semantic labels are inferred using a multi-domain convolutional architecture which operates on the image time series and which enables efficient propagation of features as well as robust model registration. To evaluate this architecture, we introduce a large-scale RGB-D dataset JHUSEQ-25 as a new benchmark for the sequence-based scene understanding in complex and densely cluttered scenes. This dataset contains 25 RGB-D video sequences with 100, 000 labeled frames in total. We validate our method on this dataset and demonstrate improved performance of semantic segmentation and 6-DoF object pose estimation compared with methods based on the single view.