Lattica：面向可扩展 AI 推理与训练的去中心化跨 NAT 通信框架
摘要
分布式 Artificial Intelligence (AI) workloads 的 rapid expansion 超出 centralized data centers, creates 对 new communication substrates 的 demand. 这些 substrates 必须在 heterogeneous and permissionless environments 中可靠运行, 其中 Network Address Translators (NATs) 和 firewalls 施加 significant constraints. Existing solutions, however, are either designed for controlled data center deployments 或 implemented as monolithic systems that tightly couple machine learning logic with networking code. 为 address these limitations, 我们提出 Lattica, 一个 decentralized cross-NAT communication framework designed to support distributed AI systems. Lattica integrates three core components. First, it employs a robust suite of NAT traversal mechanisms to establish a globally addressable peer-to-peer mesh. Second, it provides a decentralized data store based on Conflict-free Replicated Data Types (CRDTs), ensuring verifiable and eventually consistent state replication. Third, it incorporates a content discovery layer that leverages distributed hash tables (DHTs) together with an optimized RPC protocol for efficient model synchronization. By integrating these components, Lattica delivers a complete protocol stack for sovereign, resilient, and scalable AI systems that operate independently of centralized intermediaries. It is directly applicable to edge intelligence, collaborative reinforcement learning, and other large-scale distributed machine learning scenarios.
引用
@article{arxiv.2510.00183,
title = {Lattica: A Decentralized Cross-NAT Communication Framework for Scalable AI Inference and Training},
author = {Ween Yang and Jason Liu and Suli Wang and Xinyuan Song and Lynn Ai and Eric Yang and Bill Shi},
journal= {arXiv preprint arXiv:2510.00183},
year = {2025}
}