Summary of Advances and Open Challenges in Federated Foundation Models, by Chao Ren et al.
Advances and Open Challenges in Federated Foundation Models
by Chao Ren, Han Yu, Hongyi Peng, Xiaoli Tang, Bo Zhao, Liping Yi, Alysa Ziying Tan, Yulan Gao, Anran Li, Xiaoxiao Li, Zengxiang Li, Qiang Yang
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The integration of Foundation Models with Federated Learning presents a transformative paradigm in Artificial Intelligence. This paper surveys the emerging field of Federated Foundation Models, exploring novel methodologies, challenges, and future directions. It proposes a systematic multi-tiered taxonomy categorizing existing FedFM approaches for model training, aggregation, trustworthiness, and incentivization. The paper discusses key challenges including computational demands, privacy considerations, contribution evaluation, communication efficiency, and explores the potential of quantum computing to revolutionize training, inference, optimization, and security. It highlights practical applications and offers insights into the current state and challenges of FedFM, serving as a foundational guide for researchers and practitioners. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Foundation Models combine two powerful AI techniques: Foundation Models (FMs) and Federated Learning (FL). This new approach helps solve problems like privacy concerns, data decentralization, and computational efficiency. A team surveyed the field of FedFM, exploring how different methods work together and what challenges they face. They also discussed ways to make FL more efficient, secure, and trustworthy. The paper highlights some exciting applications and provides a roadmap for future research. |
Keywords
» Artificial intelligence » Federated learning » Inference » Optimization
