Title: FedAgent-HPO: Agentic Hyperparameter Optimization for Personalized Federated Learning
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: Personalized Federated learning, Hyperparameter optimization, Large language model agents, Asynchronous training, Explainability

Abstract: Hyperparameter optimization (HPO) in federated learning (FL) is especially challenging because heterogeneous clients and non-IID data make global tuning unreliable and expensive. Existing wrapper-based methods (e.g., grid search, random search, etc.) often assess numerous configurations through retraining, which is prohibitively costly in communication and computation. We propose FedAgent-HPO, a personalized and explicable framework that reframes HPO in FL as an agentic reasoning task. The framework introduces two cooperating agents. The Hyperparameter Agent generates personalized client and server configurations using local history and peer-aware signals. The Analyzer Agent dynamically adapts the search space, improving efficiency. FedAgent-HPO operates online during training and uses an asynchronous CPU–GPU pipeline to overlap agent inference with model updates, reducing wall-clock time. Built on SplitFed, it adapts to diverse client resources while producing interpretable hyperparameter trajectories. Experiments on vision, language, and cross-silo benchmarks show that FedAgent-HPO improves accuracy by up to 8.8\% under non-IID data and reduces training time through asynchronous execution.

TL;DR: We introduce FedAgent-HPO, a dual-agent framework that uses LLM reasoning to personalize hyperparameters in federated learning, improving accuracy under non-IID data and reducing training cost.