https://arxiv.org/abs/2409.05976 FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank AdaptationsThe rapid development of Large Language Models (LLMs) has been pivotal in advancing AI, with pre-trained LLMs being adaptable to diverse downstream tasks through fine-tuning. Federated learning (FL) further enhances fine-tuning in a privacy-aware manner byarxiv.orgarXiv 2024..

https://arxiv.org/abs/2206.05507 Federated Learning with GAN-based Data Synthesis for Non-IID ClientsFederated learning (FL) has recently emerged as a popular privacy-preserving collaborative learning paradigm. However, it suffers from the non-independent and identically distributed (non-IID) data among clients. In this paper, we propose a novel frameworkarxiv.org  2022 IJCAI accepted 연합학습 상황에서 ..

오늘 리뷰할 논문은 SemiFL이다. (NeurIPS 2022)  로컬 서버가 모델의 훈련을 전담하는 보통의 중앙집중식 학습방식과 다르게, 서버와 클라이언트가 연합하여 모델을 훈련하는 학습환경인 연합학습(Federated Learning)에 관한 이야기이다. 또한 supervised learning(지도학습)과 unsupervised learning(비지도학습)의 중간인 semi-supervised learning(준지도학습) 상황에서의 문제를 다루고 있다. 즉 라벨링이 되어있는 데이터와 그렇지 않은 데이터가 공존하는 상황이다.   https://arxiv.org/abs/2106.01432 SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients..