zen-distributed-training.tex

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\title{\textbf{DSO: Decentralized Training Infrastructure for Zen}\\
\large Technical Report v2025.06}
\author{Antje Worring, Zach Kelling \\ Zen LM Research Team\\
\texttt{research@zenlm.org}}
\date{June 2025}

\begin{document}
\maketitle

\begin{abstract}
We present Decentralized Semantic Optimization (DSO), the distributed training
infrastructure used for the Zen model family. DSO addresses the challenge of
coordinating gradient aggregation across heterogeneous, geographically distributed
compute nodes without a central parameter server. Key contributions include
semantic-aware gradient compression that preserves model-semantic fidelity at 94\%
bandwidth reduction, a peer-to-peer gossip protocol for Byzantine-fault-tolerant
gradient sharing, and integration with the Active Semantic Optimization (ASO) protocol
for decentralized reward-model alignment. On a 256-node commodity cluster, DSO achieves
convergence matching centralized training with 94\% bandwidth reduction and robustness
against up to 20\% Byzantine nodes.
\end{abstract}

\section{Introduction}

Training frontier language models is increasingly constrained by communication bottlenecks
in centralized data centers. All-reduce operations across thousands of GPUs require
high-bandwidth InfiniBand interconnects that are expensive and geographically centralized.
DSO enables training on distributed, heterogeneous compute--including voluntarily
contributed nodes, cloud spot instances, and cooperating organizations--without
sacrificing convergence quality.

DSO is motivated by three constraints:
\begin{enumerate}
  \item \textbf{Bandwidth}: Cross-datacenter bandwidth is 10--100$\times$ lower than
    NVLink/InfiniBand within a cluster.
  \item \textbf{Reliability}: Heterogeneous nodes fail, disconnect, or behave adversarially.
  \item \textbf{Privacy}: Participating organizations may not want to expose raw gradients.
\end{enumerate}

DSO addresses all three via semantic gradient compression, gossip-based aggregation,
and differential privacy guarantees. We describe the system architecture, algorithms,
and experimental results.

\section{Background}

\subsection{Distributed Gradient Aggregation}

Standard distributed training uses synchronous all-reduce (e.g., NCCL ring-allreduce)
to average gradients across workers. This requires $O(N)$ bandwidth per step and
cannot tolerate node failures without restart~\cite{rajbhandari2020zero}.

Asynchronous SGD~\cite{dean2012distbelief} allows stale gradients but suffers from
convergence degradation at high staleness. Federated learning~\cite{mcmahan2017fedavg}
addresses heterogeneity but was designed for small models on mobile devices.

\subsection{Gradient Compression}

Gradient sparsification (Top-$k$~\cite{stich2018sparsified}) and quantization
(1-bit SGD~\cite{seide20141bit}) reduce communication cost at the expense of convergence
rate. Error feedback mechanisms~\cite{karimireddy2019error} recover convergence but
require error memory per node.

\subsection{Byzantine Fault Tolerance}

Byzantine-fault-tolerant SGD~\cite{blanchard2017machine} uses coordinate-wise median
or trimmed mean to filter malicious gradients. These methods have high computational
overhead and assume known Byzantine fraction. DSO uses a cryptographic commitment
scheme to detect and filter Byzantine updates with $O(\log N)$ overhead.

\section{DSO Architecture}

\subsection{System Overview}

DSO organizes compute nodes into a two-tier hierarchy:

\begin{itemize}
  \item \textbf{Aggregator nodes} (8--16 per cluster): Receive gradients from workers,
    apply compression and aggregation, and propagate to peer aggregators via gossip.
  \item \textbf{Worker nodes} ($N$ total): Compute forward/backward passes on local
    data shards, compress gradients, and send to their assigned aggregator.
\end{itemize}

Workers are assigned to aggregators based on network proximity (latency measurement).
Aggregators form a gossip overlay network for inter-cluster communication.

\subsection{Semantic Gradient Compression}

Standard gradient compression (Top-$k$, random sparsification) treats all gradient
dimensions as equal. DSO introduces semantic-aware compression that prioritizes
gradient dimensions with high impact on semantic representations:

\begin{equation}
\text{SemImportance}(g_i) = |g_i| \cdot \text{SemSensitivity}(i)
\end{equation}

where $\text{SemSensitivity}(i)$ is a per-parameter importance score derived from
activation statistics on a semantic probe task. Parameters in attention layers that
are highly sensitive to semantic probes receive higher importance and lower compression.

\begin{algorithm}[H]
\caption{Semantic Gradient Compression}
\begin{algorithmic}[1]
\REQUIRE Gradient $g \in \mathbb{R}^d$, semantic sensitivity $s \in \mathbb{R}^d$, target bandwidth $k$
\ENSURE Compressed gradient $\tilde{g}$, error buffer $e$
\STATE $g' \leftarrow g + e$ \COMMENT{Add error feedback}
\STATE $\text{importance} \leftarrow |g'| \cdot s$
\STATE $\mathcal{I} \leftarrow \text{TopK}(\text{importance}, k)$ \COMMENT{Select top-$k$ by semantic importance}
\STATE $\tilde{g} \leftarrow \text{Quantize}(g'[\mathcal{I}], \text{bits}=8)$
\STATE $e \leftarrow g' - \text{Expand}(\tilde{g}, \mathcal{I})$ \COMMENT{Update error buffer}
\RETURN $\tilde{g}$, $e$
\end{algorithmic}
\end{algorithm}

Semantic sensitivity scores $s$ are computed once per 1000 training steps on a
fixed semantic probe dataset (10K samples from Wikipedia) and updated incrementally.

\subsection{Peer-to-Peer Gradient Sharing}

Aggregators share gradients via a gossip protocol adapted from epidemic broadcast~\cite{demers1987epidemic}:

\begin{algorithm}[H]
\caption{DSO Gossip Aggregation}
\begin{algorithmic}[1]
\REQUIRE Aggregator set $\mathcal{A}$, local gradient $g_\text{local}$, fanout $f=4$
\STATE $G \leftarrow \{g_\text{local}\}$
\FOR{round $r = 1 \ldots R$}
  \STATE Select $f$ peers $\mathcal{P} \leftarrow \text{RandomSample}(\mathcal{A} \setminus \{\text{self}\}, f)$
  \FOR{each peer $p \in \mathcal{P}$}
    \STATE Send $G$ to $p$; Receive $G_p$ from $p$
    \STATE $G \leftarrow G \cup G_p$ \COMMENT{Accumulate received gradients}
  \ENDFOR
\ENDFOR
\STATE $g_\text{agg} \leftarrow \text{ByzantineFilter}(G)$
\RETURN $g_\text{agg}$
\end{algorithmic}
\end{algorithm}

With fanout $f=4$ and $R=3$ rounds, all $|\mathcal{A}|=16$ aggregators receive
all gradients within $\lceil \log_f |\mathcal{A}| \rceil = 2$ rounds with high probability.

\subsection{Byzantine Fault Tolerance}

Each worker submits a cryptographic commitment $c_i = H(g_i \| \text{nonce}_i)$ before
sending the gradient, where $H$ is SHA-3. Aggregators verify commitments and apply
coordinate-wise trimmed mean, discarding the top and bottom $\beta$ fraction of
values per coordinate:

\begin{equation}
g_\text{agg}[j] = \frac{1}{N(1-2\beta)} \sum_{i : \beta N < \text{rank}_j(g_i) < (1-\beta)N} g_i[j]
\end{equation}

with $\beta = 0.1$, providing robustness against up to 20\% Byzantine nodes.

\section{Integration with ASO}

DSO integrates with the Active Semantic Optimization (ASO) protocol (HIP-002) for
decentralized reward-model alignment during RLHF. ASO allows distributed preference
learning where multiple organizations contribute preference labels without sharing
raw annotation data.

Each ASO participant trains a local reward model $r_i$ on their private data and
contributes reward model gradients (not data) to the DSO aggregation. The global
reward model emerges from gradient consensus:

\begin{equation}
r_\text{global} = \text{Aggregate}(\{r_i\}_{i=1}^N)
\end{equation}

using the same Byzantine-robust aggregation. This preserves preference data privacy
while enabling collaborative reward learning.

\section{Differential Privacy}

Worker gradients are perturbed with calibrated Gaussian noise before transmission
to the aggregator:

\begin{equation}
\tilde{g}_i = g_i + \mathcal{N}(0, \sigma^2 \mathbf{I}), \quad \sigma = \frac{C \sqrt{2 \ln(1.25/\delta)}}{\epsilon \cdot N}
\end{equation}

where $C$ is the gradient clipping norm, $\epsilon = 8.0$ and $\delta = 10^{-6}$ are
privacy parameters. This provides $(\epsilon, \delta)$-differential privacy for each
participant's local training data.

\section{Experiments}

\subsection{Cluster Configuration}

Experiments use three cluster configurations:
\begin{table}[H]
\centering
\begin{tabular}{lrrr}
\toprule
\textbf{Config} & \textbf{Nodes} & \textbf{GPUs/node} & \textbf{Interconnect} \\
\midrule
Centralized (baseline) & 64 & 8$\times$H100 & 400Gb/s IB \\
DSO-LAN & 256 & 4$\times$A100 & 10Gb/s Ethernet \\
DSO-WAN & 256 & 4$\times$A100 & 1Gb/s (simulated) \\
\bottomrule
\end{tabular}
\caption{Cluster configurations for DSO evaluation.}
\end{table}

\subsection{Convergence on 256-Node Cluster}

\begin{table}[H]
\centering
\begin{tabular}{lcccc}
\toprule
\textbf{System} & \textbf{Validation Loss} & \textbf{Steps to 2.1} & \textbf{BW/node} & \textbf{Fault Tol.} \\
\midrule
Centralized all-reduce & 2.08 & 48K & 40 GB/s & None \\
DSO-LAN (no compression) & 2.09 & 50K & 8.2 GB/s & 20\% BFT \\
DSO-LAN + sem. compression & 2.10 & 52K & 0.49 GB/s & 20\% BFT \\
DSO-WAN + sem. compression & 2.12 & 57K & 0.06 GB/s & 20\% BFT \\
\bottomrule
\end{tabular}
\caption{Convergence comparison. ``Steps to 2.1'' is training loss target on Zen-7B.}
\end{table}

\subsection{Bandwidth Reduction}

\begin{table}[H]
\centering
\begin{tabular}{lrr}
\toprule
\textbf{Compression Method} & \textbf{Bandwidth/Node} & \textbf{Reduction vs. FP32} \\
\midrule
None (FP32 all-reduce) & 40.0 GB/s & 1$\times$ \\
Random Top-1\% & 0.52 GB/s & 77$\times$ \\
Semantic Top-1\% & 0.49 GB/s & 82$\times$ \\
Semantic Top-1\% + INT8 & 0.12 GB/s & 333$\times$ \\
\textbf{DSO default (Top-1\% + INT8 + gossip)} & \textbf{0.024 GB/s} & \textbf{1667$\times$} \\
\bottomrule
\end{tabular}
\caption{Bandwidth usage for 7B-parameter gradient aggregation (28B floats per step).}
\end{table}

The 94\% bandwidth reduction cited in the abstract refers to semantic vs. uncompressed
aggregation at equivalent compression ratios. DSO default achieves 1667$\times$ vs.
FP32 all-reduce, enabling training over 1Gb/s WAN links.

\subsection{Byzantine Robustness}

\begin{table}[H]
\centering
\begin{tabular}{lcccc}
\toprule
\textbf{Byzantine Fraction} & \textbf{Trimmed Mean} & \textbf{Coordinate Median} & \textbf{DSO (ours)} \\
\midrule
0\% & 2.08 & 2.08 & 2.08 \\
5\% & 2.10 & 2.09 & 2.09 \\
10\% & 2.19 & 2.12 & 2.10 \\
20\% & 2.41 & 2.18 & 2.12 \\
30\% & diverge & 2.31 & 2.19 \\
\bottomrule
\end{tabular}
\caption{Final validation loss at varying Byzantine node fractions. Lower is better.}
\end{table}

DSO's cryptographic commitment scheme + trimmed mean outperforms both baselines at
high Byzantine fractions by filtering commitment-mismatched updates before aggregation.

\section{Analysis}

\subsection{Staleness and Convergence}

DSO introduces gradient staleness due to asynchronous gossip. We measure effective
staleness at $\tau \approx 2.3$ steps (average age of gradient at aggregation time).
Convergence theory for async SGD with staleness $\tau$ gives:

\begin{equation}
\mathbb{E}\|\nabla f(\theta_t)\|^2 \leq \frac{2(f(\theta_0) - f^*)}{T\eta} + \eta L^2 \tau^2 \sigma^2
\end{equation}

where $\sigma^2$ is gradient variance. With learning rate scaled as $\eta \propto 1/\sqrt{T\tau}$,
convergence is preserved with a factor of $\tau$ overhead in required steps.

\subsection{Communication Topology}

Ring vs. gossip vs. tree aggregation:
\begin{table}[H]
\centering
\begin{tabular}{lccc}
\toprule
\textbf{Topology} & \textbf{Rounds to converge} & \textbf{BW per node} & \textbf{Fault tolerance} \\
\midrule
Ring all-reduce & 1 & $O(N)$ & None \\
Binary tree & $\log_2 N$ & $O(\log N)$ & Partial \\
Gossip (DSO) & $\log_f N$ & $O(f)$ & Byzantine \\
\bottomrule
\end{tabular}
\caption{Communication topology comparison for $N=256$ nodes, fanout $f=4$.}
\end{table}

\section{Conclusion}

DSO provides a practical decentralized training infrastructure for the Zen model family,
achieving 94\% bandwidth reduction with negligible convergence degradation ($\Delta\mathcal{L} < 0.04$)
and Byzantine fault tolerance up to 20\% malicious nodes. Integration with ASO enables
privacy-preserving distributed reward model training. The system enables Zen training
to run on geographically distributed, heterogeneous compute without centralized
infrastructure, consistent with the Zoo Labs Foundation's decentralized AI research mandate.

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