Federated learning (FL) is an emerging machine learning paradigm that allows multiple
parties to train a shared model collaboratively in a privacy-preserving manner. Existing …

Federated learning (FL) is an emerging machine learning paradigm in which a central
server coordinates multiple participants (aka FL clients) to train a model collaboratively on …

Federated learning (FL) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …

Federated learning (FL) is widely employed for collaborative training on decentralized data
but faces challenges like data, system, and model heterogeneity. This prompted the …

Federated learning (FL) is a privacy-preserving collaboratively machine learning paradigm.
Traditional FL requires all data owners (aka FL clients) to train the same local model. This …

Recent research in federated large language models (LLMs) has primarily focused on
enabling clients to fine-tune their locally deployed homogeneous LLMs collaboratively or on …

As a privacy-preserving collaborative machine learning paradigm, federated learning (FL)
has attracted significant interest from academia and the industry alike. To allow each data …

Model-heterogeneous personalized federated learning (MHPFL) enables FL clients to train
structurally different personalized models on non-independent and identically distributed …

Data heterogeneity between clients remains a key challenge in Federated Learning (FL),
particularly in the case of tabular data. This work presents Global Layers (GL), a novel partial …

Personalized federated learning (PFL) is designed for scenarios with non-independent and
identically distributed (non-IID) client data. Existing model mixup-based methods, one of the …