Federatedscope-llm: A comprehensive package for fine-tuning large language models in federated learning
Large language models (LLMs) have demonstrated great capabilities in various natural
language understanding and generation tasks. These pre-trained LLMs can be further …
language understanding and generation tasks. These pre-trained LLMs can be further …
On the convergence of zeroth-order federated tuning for large language models
The confluence of Federated Learning (FL) and Large Language Models (LLMs) is ushering
in a new era in privacy-preserving natural language processing. However, the intensive …
in a new era in privacy-preserving natural language processing. However, the intensive …
Efficient personalized federated learning via sparse model-adaptation
Federated Learning (FL) aims to train machine learning models for multiple clients without
sharing their own private data. Due to the heterogeneity of clients' local data distribution …
sharing their own private data. Due to the heterogeneity of clients' local data distribution …
Federated learning for healthcare applications
Due to the fast advancement of artificial intelligence (AI), centralized-based models have
become critical for healthcare tasks like in medical image analysis and human behavior …
become critical for healthcare tasks like in medical image analysis and human behavior …
Federated full-parameter tuning of billion-sized language models with communication cost under 18 kilobytes
Pre-trained large language models (LLMs) require fine-tuning to improve their
responsiveness to natural language instructions. Federated learning (FL) offers a way to …
responsiveness to natural language instructions. Federated learning (FL) offers a way to …
Federated fine-tuning of large language models under heterogeneous language tasks and client resources
Federated Learning (FL) has recently been applied to the parameter-efficient fine-tuning of
Large Language Models (LLMs). While promising, it raises significant challenges due to the …
Large Language Models (LLMs). While promising, it raises significant challenges due to the …
Coala: A practical and vision-centric federated learning platform
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 …
benchmarks for practical FL scenarios, which we categorize into three levels: task, data, and …
FLHetBench: Benchmarking Device and State Heterogeneity in Federated Learning
Federated learning (FL) is a powerful technology that enables collaborative training of
machine learning models without sharing private data among clients. The fundamental …
machine learning models without sharing private data among clients. The fundamental …
Transforming educational insights: Strategic integration of federated learning for enhanced prediction of student learning outcomes
Numerous educational institutions utilize data mining techniques to manage student
records, particularly those related to academic achievements, which are essential in …
records, particularly those related to academic achievements, which are essential in …
Where is the Testbed for my Federated Learning Research?
J Božič, AR Faustino, B Radovič… - 2024 IEEE/ACM …, 2024 - ieeexplore.ieee.org
Progressing beyond centralized AI is of paramount importance, yet, distributed AI solutions,
in particular various federated learning (FL) algorithms, are often not comprehensively …
in particular various federated learning (FL) algorithms, are often not comprehensively …