AdamW has become one of the most effective optimizers for training large-scale models. We have also observed its effectiveness in the context of federated learning (FL). However, directly applying AdamW in federated learning settings poses significant challenges: (1) due to data heterogeneity, AdamW often yields high variance in the second-moment estimate v; (2) the local overfitting of AdamW may cause client drift; and (3) Reinitializing moment estimates (v, m) at each round slows down convergence. To address these challenges, we propose the first Federated AdamW algorithm, called FedAdamW, for training and fine-tuning various large models. FedAdamW aligns local updates with the global update using both a local correction mechanism and decoupled weight decay to mitigate local overfitting. FedAdamW efficiently aggregates the mean of the second-moment estimates to reduce their variance and reinitialize them. Theoretically, we prove that FedAdamW achieves a linear speedup convergence rate of O（p（L∆σ2l ）/（SKRε2） + （L∆）/R） without heterogeneity assumption, where S is the number of participating clients per round, K is the number of local iterations, and R is the total number of communication rounds. We also employ PAC-Bayesian generalization analysis to explain the effectiveness of decoupled weight decay in local training. Empirically, we validate the effectiveness of FedAdamW on language and vision Transformer models. Compared to several baselines, FedAdamW significantly reduces communication rounds and improves test accuracy.

The “Ideorealm Alignment of Paintings and Poems (IA-PP)” theory rooted in Chinese classical aesthetics offers a perspective for exploring poetry’s deep connotations. This study presents PoemPalette, a novel IA-PP creative-exploration tool that integrates Generative Artificial Intelligence (GAI) to guide poetry enthusiasts in actively constructing an ideorealm for the poetic painting they envision, informed by a formative study with six experts. We extract the core symbols of poetry, transform them into Scene Graph (SG), and generate images for users to freely compose, enabling IA-PP creative exploration. The system incorporates Large Language Model (LLM) agents to enhance the foundational understanding of poetry. In a controlled experiment on Chinese poetry and Japanese haiku with 60 participants, we analyze which interaction mechanisms most contribute to foundational understanding and creative outcomes, compared with both AI and non-AI baselines. Situated within East Asian poetry traditions, this study introduces cultural theories to guide the design of AI co-creation tools, using a graph-based interface of interpretable intermediate representations.