papers/gym-training-platform/sections/12-future-work.tex

\section{Future Work}


\subsection{Federated Learning}

Enable distributed training across institutional clusters:
\begin{itemize}
\item Privacy-preserving gradient aggregation
\item Differential privacy guarantees
\item Heterogeneous hardware support
\item Byzantine-robust aggregation
\end{itemize}

\subsection{Automatic Hyperparameter Tuning}

Integrate Optuna/Ray Tune for:
\begin{itemize}
\item Learning rate scheduling
\item LoRA rank selection
\item Batch size optimization
\item Early stopping criteria
\end{itemize}

\subsection{Model Merging and Blending}

Implement SLERP, TIES, DARE merging:
\begin{itemize}
\item Combine multiple LoRA adapters
\item Merge models from different domains
\item Weighted ensemble serving
\end{itemize}

\subsection{Knowledge Distillation}

Add distillation pipelines:
\begin{itemize}
\item Teacher-student training
\item Self-distillation for compression
\item Multi-teacher ensembles
\end{itemize}

\subsection{Enhanced Multi-Modality}

\begin{itemize}
\item Video understanding (temporal modeling)
\item 3D vision (point clouds, NeRF)
\item Robotic control (action spaces)
\item Scientific data (spectroscopy, microscopy)
\end{itemize}

\subsection{Production Tooling}

\begin{itemize}
\item A/B testing framework
\item Model monitoring dashboards
\item Drift detection
\item Automatic retraining triggers
\end{itemize}