model-aggregation

A user shares their experience using ChatGPT for complex medical caregiving and proposes the idea of aggregating multiple AI models to improve reliability by seeking consensus among different LLMs.

This paper introduces FIRMA, a family of three privacy-preserving federated learning protocols using Fibonacci-weighted ring aggregation to achieve server-free operation, permanently private classification heads, and improved accuracy under data heterogeneity.

This paper introduces GLoRA, a gauge-aware server representation for Federated LoRA that addresses the semantic mismatch in factor aggregation by estimating a consensus update subspace. Experiments show GLoRA outperforms baselines in performance and efficiency across heterogeneous client scenarios.