Large Language Models (LLMs), such as LLaMA and GPT-4, have transformed the
paradigm of natural language comprehension and generation. Despite their impressive …

Traditional Federated Learning (FL) follows a server-dominated cooperation paradigm
which narrows the application scenarios of FL and decreases the enthusiasm of data …

We present COALA, a vision-centric Federated Learning (FL) platform, and a suite of
benchmarks for practical FL scenarios, which we categorize into three levels: task, data, and …

In the absence of data protection measures, software applications lead to privacy breaches,
posing threats to end-users and software organisations. Privacy Enhancing Technologies …

Federated learning involves training statistical models over edge devices such as mobile
phones such that the training data is kept local. Federated Learning (FL) can serve as an …

Federated Learning (FL) has emerged as a promising learning approach for utilizing data
distributed across edge devices. However, existing works mainly focus on single-job FL …

Federated Learning (FL) has emerged as a promising learning approch for data distributed
across edge devices. Existing research mainly focuses on single-job FL systems. However …

We present Flotta 1, a Federated Learning framework designed to train machine learning
models on sensitive data distributed across a multi-party consortium conducting research in …