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Zekai Chen

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3 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Rising from Ashes: Generalized Federated Learning via Dynamic Parameter Reset

Jiahao Wu
Ming Hu
Yanxin Yang
Xiaofei Xie
Zekai Chen
Chenyu Song
Mingsong Chen

Although Federated Learning (FL) is promising in privacy-preserving collaborative model training, it faces low inference performance due to heterogeneous data among clients. Due to heterogeneous data in each client, FL training easily learns the specific overfitting features. Existing FL methods adopt the coarse-grained average aggregation strategy, which causes the global model to easily get stuck in local optima, resulting in low generalization of the global model. Specifically, this paper presents a novel FL framework named FedPhoenix to address this issue, which stochastically resets partial parameters to destroy some features of the global model in each round to guide the FL training to learn multiple generalized features for inference rather than specific overfitting features. Experimental results on various well-known datasets demonstrate that compared to SOTA FL methods, FedPhoenix can achieve up to 20. 73\% accuracy improvement.

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ICML Conference 2025 Conference Paper

SADA: Stability-guided Adaptive Diffusion Acceleration

Ting Jiang
Yixiao Wang
Hancheng Ye
Zishan Shao
Jingwei Sun 0002
Jingyang Zhang
Zekai Chen
Jianyi Zhang

Diffusion models have achieved remarkable success in generative tasks but suffer from high computational costs due to their iterative sampling process and quadratic-attention costs. Existing training-free acceleration strategies that reduce per-step computation cost, while effectively reducing sampling time, demonstrate low faithfulness compared to the original baseline. We hypothesize that this fidelity gap arises because (a) different prompts correspond to varying denoising trajectory, and (b) such methods do not consider the underlying ODE formulation and its numerical solution. In this paper, we propose Stability-guided Adaptive Diffusion Acceleration (SADA), a novel paradigm that unifies step-wise and token-wise sparsity decisions via a single stability criterion to accelerate sampling of ODE-based generative models (Diffusion and Flow-matching). For (a), SADA adaptively allocates sparsity based on the sampling trajectory. For (b), SADA introduces principled approximation schemes that leverage the precise gradient information from the numerical ODE solver. Comprehensive evaluations on SD-2, SDXL, and Flux using both EDM and DPM++ solvers reveal consistent $\ge 1. 8\times$ speedups with minimal fidelity degradation (LPIPS $\leq 0. 10$ and FID $\leq 4. 5$) compared to unmodified baselines, significantly outperforming prior methods. Moreover, SADA adapts seamlessly to other pipelines and modalities: It accelerates ControlNet without any modifications and speeds up MusicLDM by $1. 8\times$ with $\sim 0. 01$ spectrogram LPIPS. Our code is available at: https: //github. com/Ting-Justin-Jiang/sada-icml.

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AAAI Conference 2022 Conference Paper

ASM2TV: An Adaptive Semi-supervised Multi-Task Multi-View Learning Framework for Human Activity Recognition

Zekai Chen
Xiao Zhang
Xiuzhen Cheng

Many real-world scenarios, such as human activity recognition (HAR) in IoT, can be formalized as a multi-task multiview learning problem. Each specific task consists of multiple shared feature views collected from multiple sources, either homogeneous or heterogeneous. Common among recent approaches is to employ a typical hard/soft sharing strategy at the initial phase separately for each view across tasks to uncover common knowledge, underlying the assumption that all views are conditionally independent. On the one hand, multiple views across tasks possibly relate to each other under practical situations. On the other hand, supervised methods might be insufficient when labeled data is scarce. To tackle these challenges, we introduce a novel framework ASM2TV for semi-supervised multi-task multiview learning. We present a new perspective named gating control policy, a learnable task-view-interacted sharing policy that adaptively selects the most desirable candidate shared block for any view across any task, which uncovers more fine-grained task-view-interacted relatedness and improves inference efficiency. Significantly, our proposed gathering consistency adaption procedure takes full advantage of large amounts of unlabeled fragmented time-series, making it a general framework that accommodates a wide range of applications. Experiments on two diverse real-world HAR benchmark datasets collected from various subjects and sources demonstrate our framework’s superiority over other state-of-the-arts. The detailed codes are available at https: //github. com/zachstarkk/ASM2TV.

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