FederatedScope: A Flexible Federated Learning Platform for Heterogeneity
Open accessAbstract
Although remarkable progress has been made by existing federated learning (FL) platforms to provide infrastructures for development, these platforms may not well tackle the challenges brought by various types of heterogeneity. To fill this gap, in this paper, we propose a novel FL platform, named FederatedScope, which employs an event-driven architecture to provide users with great flexibility to independently describe the behaviors of different participants. Such a design makes it easy for users to describe participants with various local training processes, learning goals and backends, and coordinate them into an FL course with synchronous or asynchronous training strategies. Towards an easy-to-use and flexible platform, FederatedScope enables rich types of plug-in operations and components for efficient further development, and we have implemented several important components to better help users with privacy protection, attack simulation and auto-tuning. We have released FederatedScope at https://github.com/alibaba/FederatedScope to promote academic research and industrial deployment of federated learning in a wide range of scenarios.
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FedOT protects model providers' intellectual property and clients' data privacy while using distributed data to adapt LLMs to specific domains.
“Fe-dOT safeguards both the intelligent property of the model providers and the data privacy of the clients, while leveraging the distributed data for adapting LLMs to specific domains.”
From FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning (2024) · cites this paper as [64]
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Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
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Self-citationOn the Convergence of Zeroth-Order Federated Tuning for Large Language Models
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Self-citation
- Authors
- Xie, Y., Wang, Z., Gao, D., Chen, D., Yao, L., Kuang, W., Li, Y., Ding, B., & Zhou, J.
- Affiliations
- Alibaba Group (China) (CN)
- Venue
- Proceedings of the VLDB Endowment
- Published
- 2023-01
- Volume
- 16
- Issue
- 5
- Pages
- 1059-1072
- ISSN
- 2150-8097 (ISSN-L)
- OpenAlex
- W4323343894
- Cited by
- 84
- References
- 17
- Citations by year
- 2026: 7 · 2025: 28 · 2024: 31 · 2023: 18
- FWCI
- 7.92
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings of the VLDB Endowment (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- heterogeneity, event-driven architecture, participant modeling, privacy preservation, attack simulation, auto-tuning
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