Summary of Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-constrained Iot Clients, by Shaoyuan Chen et al.
Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-Constrained IoT Clients
by Shaoyuan Chen, Linlin You, Rui Liu, Shuo Yu, Ahmed M. Abdelmoniem
First submitted to arxiv on: 7 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 proposed KOALA framework addresses the challenge of fine-tuning large models in IoT settings by leveraging federated learning and knowledge distillation. To update large models while preserving privacy, KOALA utilizes small models that can run locally at IoT clients to process private data separately. The framework features two modes for joint learning: homogeneous or heterogeneous, accommodating clients with similar or different computing capacities. Experimental results show that KOALA achieves comparable training performance to conventional methods while reducing local storage and computing power requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists created a new way to update large models in devices like smart home appliances and wearables. These devices have limited resources and can’t run complex models on their own. To solve this problem, the team designed KOALA, which allows small models to run on these devices while sharing knowledge with a larger model. This approach helps preserve privacy and reduces the need for local storage and computing power. |
Keywords
* Artificial intelligence * Federated learning * Fine tuning * Knowledge distillation
