The inevitable private information in legal data necessitates legal artificial intelligence to
study privacy-preserving and decentralized learning methods. Federated learning (FL) has …

The increasing privacy concerns on personal private text data promote the development of
federated learning (FL) in recent years. However, the existing studies on applying FL in NLP …

Increasing concerns and regulations about data privacy and sparsity necessitate the study of
privacy-preserving, decentralized learning methods for natural language processing (NLP) …

When data privacy is imposed as a necessity, Federated learning (FL) emerges as a
relevant artificial intelligence field for developing machine learning (ML) models in a …

Federated learning enables training a global machine learning model from data distributed
across multiple sites, without having to move the data. This is particularly relevant in …

With increasing privacy concerns on data, recent studies have made significant progress
using federated learning (FL) on privacy-sensitive natural language processing (NLP) tasks …

LLMs have demonstrated great capabilities in various NLP tasks. Different entities can
further improve the performance of those LLMs on their specific downstream tasks by fine …

Federated learning (FL) has emerged as a highly effective paradigm for privacy-preserving
collaborative training among different parties. Unlike traditional centralized learning, which …

The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …

Federated Learning (FL) has emerged as a promising approach to address data privacy and
confidentiality concerns by allowing multiple participants to construct a shared model without …