Summary of Task-agnostic Decision Transformer For Multi-type Agent Control with Federated Split Training, by Zhiyuan Wang et al.
Task-agnostic Decision Transformer for Multi-type Agent Control with Federated Split Training
by Zhiyuan Wang, Bokui Chen, Xiaoyang Qu, Zhenhou Hong, Jing Xiao, Jianzong Wang
First submitted to arxiv on: 22 May 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 Federated Split Decision Transformer (FSDT) is a novel framework designed for AI agent decision tasks, addressing aggregation challenges posed by personalized agents. It harnesses distributed data while preserving privacy through a two-stage training process: local embedding and prediction models on clients, and a global transformer decoder model on the server. FSDT excels in federated split learning for personalized agents, offering significant reductions in communication and computational overhead compared to traditional centralized approaches. The framework demonstrates potential for efficient and privacy-preserving collaborative learning in applications like autonomous driving decision systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Split Decision Transformer is a new way to make AI better at making decisions. Right now, it’s hard to combine data from different sources because of differences between the things that are being measured. This framework helps by using data in a special way and keeping private information safe. It trains models separately on each source then combines them to get an even better model. This is helpful for things like self-driving cars that need to make decisions based on lots of different factors. |
Keywords
» Artificial intelligence » Decoder » Embedding » Transformer
