Federated Large Language Models: Current Progress and Future Directions

• URL: http://arxiv.org/abs/2409.15723v1
• Date: Tue, 24 Sep 2024 04:14:33 GMT
• Title: Federated Large Language Models: Current Progress and Future Directions
• Authors: Yuhang Yao, Jianyi Zhang, Junda Wu, Chengkai Huang, Yu Xia, Tong Yu, Ruiyi Zhang, Sungchul Kim, Ryan Rossi, Ang Li, Lina Yao, Julian McAuley, Yiran Chen, Carlee Joe-Wong,
• Abstract summary: This paper surveys Federated learning for LLMs (FedLLM), highlighting recent advances and future directions. We focus on two key aspects: fine-tuning and prompt learning in a federated setting, discussing existing work and associated research challenges.
• Score: 63.68614548512534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models are rapidly gaining popularity and have been widely adopted in real-world applications. While the quality of training data is essential, privacy concerns arise during data collection. Federated learning offers a solution by allowing multiple clients to collaboratively train LLMs without sharing local data. However, FL introduces new challenges, such as model convergence issues due to heterogeneous data and high communication costs. A comprehensive study is required to address these challenges and guide future research. This paper surveys Federated learning for LLMs (FedLLM), highlighting recent advances and future directions. We focus on two key aspects: fine-tuning and prompt learning in a federated setting, discussing existing work and associated research challenges. We finally propose potential research directions for federated LLMs, including pre-training and how LLMs can further enhance federated learning.

Related papers

• NewsInterview: a Dataset and a Playground to Evaluate LLMs' Ground Gap via Informational Interviews [65.35458530702442]
  We focus on journalistic interviews, a domain rich in grounding communication and abundant in data. We curate a dataset of 40,000 two-person informational interviews from NPR and CNN. LLMs are significantly less likely than human interviewers to use acknowledgements and to pivot to higher-level questions.
  arXiv  Detail & Related papers  (2024-11-21T01:37:38Z)
• Personalized Wireless Federated Learning for Large Language Models [75.22457544349668]
  Large Language Models (LLMs) have revolutionized natural language processing tasks. Their deployment in wireless networks still face challenges, i.e., a lack of privacy and security protection mechanisms. We introduce two personalized wireless federated fine-tuning methods with low communication overhead.
  arXiv  Detail & Related papers  (2024-04-20T02:30:21Z)
• Cross-Data Knowledge Graph Construction for LLM-enabled Educational Question-Answering System: A Case Study at HCMUT [2.8000537365271367]
  Large language models (LLMs) have emerged as a vibrant research topic. LLMs face challenges in remembering events, incorporating new information, and addressing domain-specific issues or hallucinations. This article proposes a method for automatically constructing a Knowledge Graph from multiple data sources.
  arXiv  Detail & Related papers  (2024-04-14T16:34:31Z)
• Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges [47.45993726498343]
  Data augmentation (DA) has emerged as a pivotal technique for enhancing model performance by diversifying training examples without the need for additional data collection. This survey explores the transformative impact of large language models (LLMs) on DA, particularly addressing the unique challenges and opportunities they present in the context of natural language processing (NLP) and beyond.
  arXiv  Detail & Related papers  (2024-03-05T14:11:54Z)
• Continual Learning for Large Language Models: A Survey [95.79977915131145]
  Large language models (LLMs) are not amenable to frequent re-training, due to high training costs arising from their massive scale. This paper surveys recent works on continual learning for LLMs.
  arXiv  Detail & Related papers  (2024-02-02T12:34:09Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• Challenges and Contributing Factors in the Utilization of Large Language Models (LLMs) [10.039589841455136]
  This review explores the issue of domain specificity, where large language models (LLMs) may struggle to provide precise answers to specialized questions within niche fields. It's suggested to diversify training data, fine-tune models, enhance transparency and interpretability, and incorporate ethics and fairness training.
  arXiv  Detail & Related papers  (2023-10-20T08:13:36Z)
• Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly [62.473245910234304]
  This paper takes a hardware-centric approach to explore how Large Language Models can be brought to modern edge computing systems. We provide a micro-level hardware benchmark, compare the model FLOP utilization to a state-of-the-art data center GPU, and study the network utilization in realistic conditions.
  arXiv  Detail & Related papers  (2023-10-04T20:27:20Z)
• Evaluating the Communication Efficiency in Federated Learning Algorithms [3.713348568329249]
  Recently, in light of new privacy legislations in many countries, the concept of Federated Learning (FL) has been introduced. In FL, mobile users are empowered to learn a global model by aggregating their local models, without sharing the privacy-sensitive data. This raises the challenge of communication cost when implementing FL at large scale.
  arXiv  Detail & Related papers  (2020-04-06T15:31:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.