Reproduce “FedAgentBench: Towards Automating Real-world Federated Medical Image Analysis with Server-Client LLM Agents” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Pramit Saha, Joshua Strong, Divyanshu Mishra, Cheng Ouyang, J.Alison Noble
The research introduces FedAgentBench, a framework and benchmark utilizing LLM agents to automate operational complexities in federated medical image analysis. It demonstrates that advanced LLMs can orchestrate tasks like client selection, data pre-processing, and label harmonization, achieving success rates up to 100% for top proprietary models and up to 94% for leading open-source models in specific stages, significantly reducing manual intervention.
Key insights
- LLM agents can effectively automate many complex operational challenges in Federated Learning, particularly with fine-grained guidance, suggesting a path toward more accessible and scalable medical AI.
- While proprietary LLMs (e.g., GPT-4.1) generally outperform open-source models, strong open-source models like DeepSeek-V3 and Qwen QwQ 32B demonstrate competitive performance in specific tasks.
- Label harmonization and robust data pre-processing remain the most challenging tasks for current LLM agents, highlighting limitations in domain-specific reasoning and multi-step planning.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.