Compared with general Federated Learning (FL), Decentralized FL (DFL) has diminished central communication burdens and lower risks of disruption. To be compatible with real-world scenarios, some existing works produce several local personalized models rather than a universal model for all edge devices. However, they still suffer from inferior performance due to the full model aggregation in heterogeneous datasets. Therefore, we propose a DFL framework DFedMDC through partial model personalization, which can adapt to resource-heterogeneous environments (e.g., the Internet of Things (IoT)). Specifically, it personalizes the “right” components of local models and trains the shared modeluiand personal modelvialternately in each clienti. To further accelerate the convergence process, we propose DFedSMDC with properly directed noise perturbation into the gradient update. Theoretically, we provide convergence analysis of both algorithms in the general non-convex setting. It can shed light on how vital factors affect the convergence rate, such as these alternate updates of partial modelsui,vi, data heterogeneity δ2as well as various communication topologies (characterized by the spectral gap 1 − λ). Empirically, we confirm the state-of-the-art (SOTA) superiority of the proposed methods on several real-world datasets with various data distributions, relative to both SOTA personalized FL (PFL) and DFL baselines.

based on instance-specific, reasoning-oriented evaluation questions that assess three critical dimensions: text-image alignment, reasoning accuracy, and image quality.Extensive experiments with 17 representative T2I models, including a strong pipeline-based framework that decouples reasoning and generation using the state-of-the-art language and image generation models, demonstrate consistently limited reasoning performance, highlighting the need for more robust, reasoning-aware architectures in the next generation of T2I systems.

Understanding how knowledge is distributed across the layers of generative models is crucial for improving interpretability, controllability, and adaptation. While prior work has explored knowledge localization in UNet-based architectures, Diffusion Transformer (DiT)-based models remain underexplored in this context. In this paper, we propose a model- and knowledge-agnostic method to localize where specific types of knowledge are encoded within the DiT blocks. We evaluate our method on state-of-the-art DiT-based models, including PixArt-$\alpha$, FLUX, and SANA, across six diverse knowledge categories. We show that the identified blocks are both interpretable and causally linked to the expression of knowledge in generated outputs. Building on these insights, we apply our localization framework to two key applications: *model personalization* and *knowledge unlearning*. In both settings, our localized fine-tuning approach enables efficient and targeted updates, reducing computational cost, improving task-specific performance, and better preserving general model behavior with minimal interference to unrelated or surrounding content. Overall, our findings offer new insights into the internal structure of DiTs and introduce a practical pathway for more interpretable, efficient, and controllable model editing.

Zihao Lin, Mohammad Beigi, Hongxuan Li, Yufan Zhou, Yuxiang Zhang, Qifan Wang, Wenpeng Yin, Lifu Huang. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Interleaved text-and-image generation has been an intriguing research direction, where the models are required to generate both images and text pieces in an arbitrary order.Despite the emerging advancements in interleaved generation, the progress in its evaluation still significantly lags behind.Existing evaluation benchmarks do not support arbitrarily interleaved images and text for both inputs and outputs, and they only cover a limited number of domains and use cases.Also, current works predominantly use similarity-based metrics which fall short in assessing the quality in open-ended scenarios.To this end, we introduce INTER-LEAVEDBENCH, the first benchmark carefully curated for the evaluation of interleaved textand-image generation.INTERLEAVEDBENCH features a rich array of tasks to cover diverse real-world use cases.In addition, we present INTERLEAVEDEVAL, a strong reference-free metric powered by GPT-4o to deliver accurate and explainable evaluation.We carefully define five essential evaluation aspects for IN-TERLEAVEDEVAL, including text quality, perceptual quality, image coherence, text-image coherence, and helpfulness, to ensure a comprehensive and fine-grained assessment.Through extensive experiments and rigorous human evaluation, we show that our benchmark and metric can effectively evaluate the existing models with a strong correlation with human judgments surpassing previous reference-based metrics.We also provide substantial findings and insights to foster future research in interleaved generation and its evaluation. 1