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227 lines
8.8 KiB
TeX
227 lines
8.8 KiB
TeX
\section{Reproducibility Details}
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\label{app:reproducibility}
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\begin{itemize}[leftmargin=1.5em]
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\item \textbf{Environment:} Python 3.11, LanceDB 0.3.0, sentence-transformers
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1.2.0 (all-MiniLM-L6-v2, $d=384$; we use $d=768$ in the paper via
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all-mpnet-base-v2).
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\item \textbf{Network simulation:} Discrete-event simulator with configurable
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latency (uniform 10--100\,ms), packet loss ($p=0.01$), and node failures.
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\item \textbf{Byzantine behavior:} Random noise ($\bm{s}_{i,m}^{(j)} \sim
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\text{Unif}(\{-1,+1\}^g)$, $\alpha_{i,m}^{(j)} \sim \text{Unif}(0, 1)$).
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\item \textbf{GRPO setup:} Group size $G = 4$, max iterations $K = 3$,
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reward weights $\alpha = 0.7$, $\beta = 0.3$.
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\item \textbf{Seeds:} Fixed seeds 42, 123, 456 for three independent runs;
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all tables report mean $\pm$ std ($\leq 0.4\%$ in all cases).
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\item \textbf{Hardware:} Simulated on a single 16-core x86 machine;
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no GPU required for simulation.
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\end{itemize}
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% ============================================================================
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% REFERENCES
|
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% ============================================================================
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\bibliographystyle{plainnat}
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\begin{thebibliography}{40}
|
|
|
|
\bibitem[Baker et~al.(2020)]{baker2020emergent}
|
|
Baker, B., Kanitscheider, I., Marber, T., Wu, Y., Powell, G., McGrew, B.,
|
|
and Mordatch, I.
|
|
\newblock Emergent tool use from multi-agent autocurricula.
|
|
\newblock \emph{ICLR}, 2020.
|
|
|
|
\bibitem[Bernstein et~al.(2018)]{bernstein2018signsgd}
|
|
Bernstein, J., Wang, Y.-X., Azizzadenesheli, K., and Anandkumar, A.
|
|
\newblock signSGD: Compressed optimisation for non-convex problems.
|
|
\newblock \emph{ICML}, 2018.
|
|
|
|
\bibitem[Bittensor(2023)]{bittensor2023}
|
|
Bittensor.
|
|
\newblock Bittensor: A peer-to-peer intelligence market.
|
|
\newblock Whitepaper, 2023.
|
|
|
|
\bibitem[Blanchard et~al.(2017)]{blanchard2017machine}
|
|
Blanchard, P., El~Mhamdi, E.~M., Guerraoui, R., and Stainer, J.
|
|
\newblock Machine learning with adversaries: Byzantine tolerant gradient
|
|
descent.
|
|
\newblock \emph{NeurIPS}, 2017.
|
|
|
|
\bibitem[Bonawitz et~al.(2017)]{bonawitz2017practical}
|
|
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H.~B.,
|
|
Patel, S., Ramage, D., Segal, A., and Seth, K.
|
|
\newblock Practical secure aggregation for privacy-preserving machine learning.
|
|
\newblock \emph{ACM CCS}, 2017.
|
|
|
|
\bibitem[Chen et~al.(2018)]{chen2018draco}
|
|
Chen, L., Wang, H., Charles, Z., and Papailiopoulos, D.
|
|
\newblock DRACO: Byzantine-resilient distributed training via redundant
|
|
gradients.
|
|
\newblock \emph{ICML}, 2018.
|
|
|
|
\bibitem[Christianos et~al.(2021)]{christianos2021scaling}
|
|
Christianos, F., Papoudakis, G., Rahman, M.~A., and Albrecht, S.~V.
|
|
\newblock Scaling multi-agent reinforcement learning with selective parameter
|
|
sharing.
|
|
\newblock \emph{ICML}, 2021.
|
|
|
|
\bibitem[Conneau et~al.(2020)]{conneau2020unsupervised}
|
|
Conneau, A., Rinott, R., Lample, G., Williams, A., Bowman, S., Schwenk, H.,
|
|
and Stoyanov, V.
|
|
\newblock XNLI: Evaluating cross-lingual sentence representations.
|
|
\newblock \emph{EMNLP}, 2020.
|
|
|
|
\bibitem[Demers et~al.(1987)]{demers1987epidemic}
|
|
Demers, A., Greene, D., Hauser, C., Irish, W., Larson, J., Shenker, S.,
|
|
Sturgis, H., Swinehart, D., and Terry, D.
|
|
\newblock Epidemic algorithms for replicated database maintenance.
|
|
\newblock \emph{PODC}, 1987.
|
|
|
|
\bibitem[Dwork \& Roth(2014)]{dwork2014algorithmic}
|
|
Dwork, C. and Roth, A.
|
|
\newblock The algorithmic foundations of differential privacy.
|
|
\newblock \emph{Foundations and Trends in Theoretical Computer Science},
|
|
9(3--4):211--407, 2014.
|
|
|
|
\bibitem[Furlanello et~al.(2018)]{furlanello2018born}
|
|
Furlanello, T., Lipton, Z.~C., Tschannen, M., Itti, L., and Anandkumar, A.
|
|
\newblock Born again neural networks.
|
|
\newblock \emph{ICML}, 2018.
|
|
|
|
\bibitem[Gensyn(2023)]{gensyn2023}
|
|
Gensyn.
|
|
\newblock Gensyn: Protocol for decentralized ML computation.
|
|
\newblock Whitepaper, 2023.
|
|
|
|
\bibitem[Hanzo AI Research(2026a)]{hanzo2026aso}
|
|
Hanzo AI Research.
|
|
\newblock Active Semantic Optimization: Training-free adaptation via Bayesian
|
|
Product-of-Experts decoding.
|
|
\newblock Technical report, Hanzo AI, 2026.
|
|
|
|
\bibitem[Hanzo AI Research(2026b)]{hanzo2026dso}
|
|
Hanzo AI Research.
|
|
\newblock Decentralized Semantic Optimization: Byzantine-robust prior
|
|
aggregation for collective model adaptation.
|
|
\newblock Technical report, Hanzo AI (ZIP-001/400), 2026.
|
|
|
|
\bibitem[Hanzo AI Research(2026c)]{hanzo2026bitdelta}
|
|
Hanzo AI Research.
|
|
\newblock BitDelta: 1-bit quantization of experience embedding deltas for
|
|
efficient fleet communication.
|
|
\newblock Technical report, Hanzo AI (ZIP-007), 2026.
|
|
|
|
\bibitem[Hanzo AI Research(2026d)]{hanzo2026poai}
|
|
Hanzo AI Research.
|
|
\newblock Proof of AI: Verifiable attestation for agent trajectory extraction.
|
|
\newblock Technical report, Hanzo AI (ZIP-002), 2026.
|
|
|
|
\bibitem[Hinton(2002)]{hinton2002training}
|
|
Hinton, G.~E.
|
|
\newblock Training products of experts by minimizing contrastive divergence.
|
|
\newblock \emph{Neural Computation}, 14(8):1771--1800, 2002.
|
|
|
|
\bibitem[Hinton et~al.(2015)]{hinton2015distilling}
|
|
Hinton, G., Vinyals, O., and Dean, J.
|
|
\newblock Distilling the knowledge in a neural network.
|
|
\newblock \emph{NeurIPS Workshop}, 2015.
|
|
|
|
\bibitem[Jimenez et~al.(2024)]{jimenez2024swebench}
|
|
Jim{\'e}nez, C.~E., Yang, J., Wettig, A., Yao, S., Pei, K., Press, O.,
|
|
and Narasimhan, K.
|
|
\newblock SWE-bench: Can language models resolve real-world {GitHub} issues?
|
|
\newblock \emph{ICLR}, 2024.
|
|
|
|
\bibitem[Koloskova et~al.(2020)]{koloskova2020unified}
|
|
Koloskova, A., Stich, S.~U., and Jaggi, M.
|
|
\newblock Decentralized stochastic optimization and gossip algorithms with
|
|
compressed communication.
|
|
\newblock \emph{ICML}, 2020.
|
|
|
|
\bibitem[Lamport et~al.(1982)]{lamport1982byzantine}
|
|
Lamport, L., Shostak, R., and Pease, M.
|
|
\newblock The Byzantine generals problem.
|
|
\newblock \emph{ACM TOPLAS}, 4(3):382--401, 1982.
|
|
|
|
\bibitem[LanceDB(2024)]{lancedb2024}
|
|
LanceDB.
|
|
\newblock LanceDB: Serverless vector database for AI applications.
|
|
\newblock \url{https://lancedb.com}, 2024.
|
|
|
|
\bibitem[Li et~al.(2020)]{li2020federated}
|
|
Li, T., Sahu, A.~K., Zaheer, M., Sanjabi, M., Talwalkar, A., and
|
|
Smith, V.
|
|
\newblock Federated optimization in heterogeneous networks.
|
|
\newblock \emph{MLSys}, 2020.
|
|
|
|
\bibitem[Liu et~al.(2024)]{liu2024bitdelta}
|
|
Liu, J., Kulikov, R., Fox, Z., Nikdan, A., Kim, Y., Papailiopoulos, D.,
|
|
and De~Sa, C.
|
|
\newblock BitDelta: Your fine-tune may only be worth one bit.
|
|
\newblock \emph{arXiv preprint arXiv:2402.10193}, 2024.
|
|
|
|
\bibitem[McMahan et~al.(2017)]{mcmahan2017communication}
|
|
McMahan, H.~B., Moore, E., Ramage, D., Hampson, S., and
|
|
Ag{\"u}era~y~Arcas, B.
|
|
\newblock Communication-efficient learning of deep networks from decentralized
|
|
data.
|
|
\newblock \emph{AISTATS}, 2017.
|
|
|
|
\bibitem[McMahan et~al.(2018)]{mcmahan2018learning}
|
|
McMahan, H.~B., Ramage, D., Talwar, K., and Zhang, L.
|
|
\newblock Learning differentially private recurrent language models.
|
|
\newblock \emph{ICLR}, 2018.
|
|
|
|
\bibitem[El~Mhamdi et~al.(2018)]{mhamdi2018hidden}
|
|
El~Mhamdi, E.~M., Guerraoui, R., and Rouault, S.
|
|
\newblock The hidden vulnerability of distributed learning in Byzantium.
|
|
\newblock \emph{ICML}, 2018.
|
|
|
|
\bibitem[Minsker(2015)]{minsker2015geometric}
|
|
Minsker, S.
|
|
\newblock Geometric median and robust estimation in Banach spaces.
|
|
\newblock \emph{Bernoulli}, 21(4):2308--2335, 2015.
|
|
|
|
\bibitem[Pease et~al.(1980)]{pease1980reaching}
|
|
Pease, M., Shostak, R., and Lamport, L.
|
|
\newblock Reaching agreement in the presence of faults.
|
|
\newblock \emph{Journal of the ACM}, 27(2):228--234, 1980.
|
|
|
|
\bibitem[Seide et~al.(2014)]{seide20141bit}
|
|
Seide, F., Fu, H., Droppo, J., Li, G., and Yu, D.
|
|
\newblock 1-bit stochastic gradient descent and its application to
|
|
data-parallel distributed training of speech DNNs.
|
|
\newblock \emph{Interspeech}, 2014.
|
|
|
|
\bibitem[Shao et~al.(2024)]{shao2024deepseekmath}
|
|
Shao, Z., Wang, P., Zhu, Q., Xu, R., Song, J., Bi, X., Zhang, H., Zhang, M.,
|
|
Li, Y.~K., Wu, Y., and Guo, D.
|
|
\newblock DeepSeekMath: Pushing the limits of mathematical reasoning in open
|
|
language models.
|
|
\newblock \emph{arXiv preprint arXiv:2402.03300}, 2024.
|
|
|
|
\bibitem[Stich et~al.(2018)]{stich2018sparsified}
|
|
Stich, S.~U., Cordonnier, J.-B., and Jaggi, M.
|
|
\newblock Sparsified SGD with memory.
|
|
\newblock \emph{NeurIPS}, 2018.
|
|
|
|
\bibitem[Warnat-Herresthal et~al.(2021)]{warnat2021swarm}
|
|
Warnat-Herresthal, S., Schultze, H., Shastry, K.~L., et~al.
|
|
\newblock Swarm learning for decentralized and confidential clinical machine
|
|
learning.
|
|
\newblock \emph{Nature}, 594:265--270, 2021.
|
|
|
|
\bibitem[Yin et~al.(2018)]{yin2018byzantine}
|
|
Yin, D., Chen, Y., Kannan, R., and Bartlett, P.
|
|
\newblock Byzantine-robust distributed learning: Towards optimal statistical
|
|
rates.
|
|
\newblock \emph{ICML}, 2018.
|
|
|
|
\bibitem[Zhang et~al.(2024)]{zhang2024fedlora}
|
|
Zhang, J., Hua, Y., Wang, H., Song, T., Xue, Z., Ma, R., and Guan, H.
|
|
\newblock FedLoRA: When personalized federated learning meets low-rank
|
|
adaptation.
|
|
\newblock \emph{arXiv preprint arXiv:2307.06768}, 2024.
|
|
|
|
\end{thebibliography}
|
|
|