Federated Learning (FL) has been foundational in improving the performance of a wide
range of applications since it was first introduced by Google. Some of the most prominent …

Federated Learning aims to learn machine learning models from multiple decentralized
edge devices (eg mobiles) or servers without sacrificing local data privacy. Recent Natural …

Federated learning (FL) has emerged as a secure paradigm for collaborative training among
clients. Without data centralization, FL allows clients to share local information in a privacy …

Federated learning (FL) has enabled global model training on decentralized data in a
privacy-preserving way. However, for tasks that utilize pre-trained language models (PLMs) …

While federated learning (FL) is promising for efficient collaborative learning without
revealing local data, it remains vulnerable to white-box privacy attacks, suffers from high …

Most studies in cross-device federated learning focus on small models, due to the server-
client communication and on-device computation bottlenecks. In this work, we leverage …

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 …

We revisit the problem of federated learning (FL) with private data from people who do not
trust the server or other silos/clients. In this context, every silo (eg hospital) has data from …

There has been growing concern regarding data privacy during the development and
deployment of Multimodal Foundation Models for Artificial General Intelligence (AGI), while …

Pre-trained BERT models have achieved impressive performance in many natural language
processing (NLP) tasks. However, in many real-world situations, textual data are usually …