This paper focuses on addressing the practical yet challenging problem of model
heterogeneity in federated learning, where clients possess models with different network …

Recently model merging techniques have surfaced as a solution to combine multiple single-
talent models into a single multi-talent model. However previous endeavors in this field have …

Sampling from diffusion probabilistic models (DPMs) is often expensive for high-quality
image generation and typically requires many steps with a large model. In this paper, we …

Advancements in LLMs have recently unveiled challenges tied to computational efficiency
and continual scalability due to their requirements of huge parameters, making the …

Federated learning, a pioneering paradigm, enables collaborative model training without
exposing users' data to central servers. Most existing federated learning systems necessitate …

The paradigm of pre-training and fine-tuning has laid the foundation for deploying deep
learning models. However most fine-tuning methods are designed to meet a specific …

Unmanned surface vehicles (USVs) are distinguished by their intelligence, compactness,
and absence of human casualties, making them a vital component of the maritime industry …

Age-related macular degeneration (AMD) is an eye disease that leads to the deterioration of
the central vision area of the eye and can gradually result in vision loss in elderly …

For the sixth-generation (6G) mobile networks, in-situ model downloading has emerged as
an important use case to enable real-time adaptive artificial intelligence (AI) on edge …

In recent years, the merging of vast datasets with powerful computational resources has led
to the emergence of large pre-trained models in the field of deep learning. However, the …