Large Language Models (LLMs) achieve state-of-the-art performance but are challenging to
deploy due to their high computational and storage demands. Pruning can reduce model …

In federated learning (FL), accommodating clients' varied computational capacities poses a
challenge, often limiting the participation of those with constrained resources in global …

Prompt learning for pre-trained Vision-Language Models (VLMs) like CLIP has
demonstrated potent applicability across diverse downstream tasks. This lightweight …

Distributed Collaborative Machine Learning (DCML) has emerged in artificial intelligence-
empowered edge computing environments, such as the Industrial Internet of Things (IIoT), to …

Conventional federated learning (FL) assumes the homogeneity of models, necessitating
clients to expose their model parameters to enhance the performance of the server model …

Virtually all federated learning (FL) methods, including FedAvg, operate in the following
manner: i) an orchestrating server sends the current model parameters to a cohort of clients …

In the field of heterogeneous federated learning (FL), the key challenge is to efficiently and
collaboratively train models across multiple clients with different data distributions, model …