OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework
Recent developments in Artificial Intelligence techniques have enabled their successful application across a spectrum of commercial and industrial settings. However, these techniques require large volumes of data to be aggregated in a centralized manner, forestalling their applicability to scenarios wherein the data is sensitive or the cost of data transmission is prohibitive. Federated Learning alleviates these problems by decentralizing model training, thereby removing the need for data transfer and aggregation. To advance the adoption of Federated Learning, more research and development needs to be conducted to address some important open questions. In this work, we propose OpenFed, an open-source software framework for end-to-end Federated Learning. OpenFed reduces the barrier to entry for both researchers and downstream users of Federated Learning by the targeted removal of existing pain points. For researchers, OpenFed provides a framework wherein new methods can be easily implemented and fairly evaluated against an extensive suite of benchmarks. For downstream users, OpenFed allows Federated Learning to be plugged and play within different subject-matter contexts, removing the need for deep expertise in Federated Learning.
Code (1)
Tasks
Federated LearningSimilar Papers 제목 키워드 기반
OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning
Trained on massive publicly available data, large language models (LLMs) have demonstrated tremendous success across various fields. While more data contributes to better performance, a disconcerting reality is that high…
Federated LearningInstruction FollowingPrivacy PreservingExploring Federated Unlearning: Review, Comparison, and Insights
The increasing demand for privacy-preserving machine learning has spurred interest in federated unlearning, which enables the selective removal of data from models trained in federated systems. However, developing federa…
Federated LearningPrivacy PreservingSurveyTowards Verifiable Federated Learning
Federated learning (FL) is an emerging paradigm of collaborative machine learning that preserves user privacy while building powerful models. Nevertheless, due to the nature of open participation by self-interested entit…
Federated LearningFederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning
LLMs have demonstrated great capabilities in various NLP tasks. Different entities can further improve the performance of those LLMs on their specific downstream tasks by fine-tuning LLMs. When several entities have simi…
BenchmarkingFederated LearningLarge Language Modelparameter-efficient fine-tuningFLIP: Towards Comprehensive and Reliable Evaluation of Federated Prompt Learning
The increasing emphasis on privacy and data security has driven the adoption of federated learning, a decentralized approach to train machine learning models without sharing raw data. Prompt learning, which fine-tunes pr…
Federated LearningPrompt Learning