Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
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 …
When foundation model meets federated learning: Motivations, challenges, and future directions
The intersection of the Foundation Model (FM) and Federated Learning (FL) provides mutual
benefits, presents a unique opportunity to unlock new possibilities in AI research, and …
benefits, presents a unique opportunity to unlock new possibilities in AI research, and …
Fedbiot: Llm local fine-tuning in federated learning without full model
Large language models (LLMs) show amazing performance on many domain-specific tasks
after fine-tuning with some appropriate data. However, many domain-specific data are …
after fine-tuning with some appropriate data. However, many domain-specific data are …
Federated learning: Overview, strategies, applications, tools and future directions
Federated learning (FL) is a distributed machine learning process, which allows multiple
nodes to work together to train a shared model without exchanging raw data. It offers several …
nodes to work together to train a shared model without exchanging raw data. It offers several …
Federatedscope-gnn: Towards a unified, comprehensive and efficient package for federated graph learning
The incredible development of federated learning (FL) has benefited various tasks in the
domains of computer vision and natural language processing, and the existing frameworks …
domains of computer vision and natural language processing, and the existing frameworks …
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 …
Fs-real: Towards real-world cross-device federated learning
Federated Learning (FL) aims to train high-quality models in collaboration with distributed
clients while not uploading their local data, which attracts increasing attention in both …
clients while not uploading their local data, which attracts increasing attention in both …
Revisiting personalized federated learning: Robustness against backdoor attacks
In this work, besides improving prediction accuracy, we study whether personalization could
bring robustness benefits to backdoor attacks. We conduct the first study of backdoor attacks …
bring robustness benefits to backdoor attacks. We conduct the first study of backdoor attacks …