papers/hanzo-federated-agents/hanzo-federated-agents.tex

% Hanzo Federated Agents Paper
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\title{Federated Agent Intelligence:\\
Decentralized Experience Sharing via\\
BitDelta-Compressed Semantic Optimization}
\author{
    Antje Worring, Zach Kelling \\
    Hanzo AI Research \quad Zoo Labs Foundation\\
    \textit{Hanzo AI Inc (Techstars '17), Los Angeles, CA}\\
    \texttt{[email protected]}
}
\date{February 2026}

\begin{document}

\hanzocoverpage

% ============================================================================
% ABSTRACT
% ============================================================================
\begin{abstract}
We present \textbf{Federated Agent Intelligence (FAI)}, a system for
collective learning across autonomous AI agent fleets without sharing model
weights, raw trajectories, private data, or training compute. Each agent node
runs local Group-Relative Policy Optimization (GRPO) to extract
semantic experiences from tool-use trajectories, compresses these experiences
via \textbf{BitDelta} (1-bit quantization of embedding deltas yielding
${\approx}10\times$ compression), and broadcasts them to the fleet via
\textbf{DSO} (Decentralized Semantic Optimization, ZIP-001/400).
At the receiving end, \textbf{DeltaSoup} performs Byzantine-robust median
aggregation to filter adversarial contributions before merging into the local
experience library.

We prove that the FAI protocol converges to a near-optimal shared experience
library under an honest majority ($f < N/3$ Byzantine nodes) and that the
BitDelta compression-decompression cycle preserves semantic cosine similarity
with expected loss below 5\%. Fleet-level experiments with $N \in \{10, 50, 100\}$
nodes demonstrate \textbf{23.7\% improvement} in task success rate over isolated
agents at $N=100$, with graceful degradation under Byzantine loads up to $N/3$.
The BitDelta wire format reduces per-broadcast bandwidth from 2.4\,MB to 240\,KB,
enabling practical operation over commodity peer-to-peer networks. The complete
system is implemented as a plugin for the Hanzo agent harness and operates
continuously alongside normal agent sessions without interrupting agent execution.
\end{abstract}

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% 1. INTRODUCTION
% ============================================================================

\input{sections/01-introduction}
\input{sections/02-background}
\input{sections/03-system-architecture}
\input{sections/04-bitdelta-experience-compression}
\input{sections/05-deltasoup-byzantine-robust-aggregation}
\input{sections/06-fleet-learning-dynamics}
\input{sections/07-integration-with-the-hanzo-agent-harness}
\input{sections/08-privacy-and-security}
\input{sections/09-experimental-evaluation}
\input{sections/10-discussion-and-future-work}
\input{sections/11-related-work}
\input{sections/12-conclusion}
\input{sections/13-proof-of-coordinate-wise-median-robustne}
\input{sections/14-deltasoup-detailed-filter-analysis}
\input{sections/15-bandwidth-analysis}
\input{sections/99-reproducibility-details}

\end{document}