papers/zoo-network-architecture/sections/06-decentralized-model-training.tex

\section{Decentralized Model Training}


\subsection{Federated Learning}

Zoo supports federated learning \cite{mcmahan2017federated} where data remains on contributor devices:

\begin{enumerate}
\item \textbf{Initialization}: Coordinator publishes initial model $\theta_0$ and experience library $E_0$ on IPFS.
\item \textbf{Local Training}: Participants download $\theta_0$ and $E_0$, perform Training-Free GRPO locally with their data, produce experience updates $\Delta E_i$.
\item \textbf{Aggregation}: Coordinator collects $\{\Delta E_i\}$, runs batch consolidation (Section 4.2.3), produces $E_1$.
\item \textbf{Distribution}: Updated library $E_1$ published on IPFS; participants download for next round.
\end{enumerate}

Unlike traditional federated learning (which aggregates gradients), Zoo aggregates \textit{semantic experiences}. This is more efficient (natural language is compact) and interpretable (humans can audit experiences).

\subsection{Privacy-Preserving Techniques}

\subsubsection{Differential Privacy}

Experiences can be sanitized via differential privacy \cite{dwork2014algorithmic}:
\[
\Pr[A(E) \in S] \leq e^\epsilon \Pr[A(E') \in S] + \delta
\]
Noise is added to experience embeddings or metadata to prevent inference attacks.

\subsubsection{Zero-Knowledge Proofs}

For sensitive experiences, contributors can submit zero-knowledge proofs of correctness without revealing content. For example, a zkSNARK \cite{bitansky2013snarks} proving "this experience improves validation accuracy by $\geq 5\%$" without disclosing the experience text.

\subsubsection{Secure Multi-Party Computation}

Multiple parties jointly compute experience consolidation without revealing individual contributions. This is particularly useful for medical/financial domains where data is regulated.

\subsection{Incentive Alignment}

Contributors are rewarded via:
\begin{itemize}
\item \textbf{Inference Credits}: 1 hour of contribution $\approx$ 1000 free inference queries.
\item \textbf{KEEPER Tokens}: Proportional to upvotes received on submitted experiences.
\item \textbf{Revenue Share}: 10\% of API usage fees distributed to top contributors.
\end{itemize}

This creates a virtuous cycle: high-quality contributions improve models, attract more users, generate more revenue, reward contributors.