惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

WordPress大学
WordPress大学
GbyAI
GbyAI
P
Proofpoint News Feed
B
Blog
MyScale Blog
MyScale Blog
V
V2EX
B
Blog RSS Feed
Microsoft Security Blog
Microsoft Security Blog
量子位
Jina AI
Jina AI
博客园 - 叶小钗
Recent Announcements
Recent Announcements
有赞技术团队
有赞技术团队
罗磊的独立博客
L
LangChain Blog
I
InfoQ
云风的 BLOG
云风的 BLOG
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
人人都是产品经理
人人都是产品经理
小众软件
小众软件
V
Visual Studio Blog
月光博客
月光博客
The Cloudflare Blog
雷峰网
雷峰网

cs.DC updates on arXiv.org

DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
Intercloud: Eventual Consistency for Decentralised Econom...
Gregory Magarshak · 2026-04-21 · via cs.DC updates on arXiv.org

We present Intercloud, a decentralised economic network in which streams of private data are secured by Watcher swarms that observe only cryptographic hashes, never plaintext. Intercloud requires no global consensus beyond a single shared random seed per epoch. Two mechanisms provide security: (i) ripple deduplication via epoch-stamped identifiers, preventing any ripple from propagating through the same node twice per epoch, guaranteeing termination without global coordination; and (ii) chilling-effect consensus, in which a swarm reaches finality by attesting to the absence of conflicting evidence rather than voting between alternatives. Any conflicting attestation automatically yields a self-certifying Proof of Corruption. We prove four main results. First, execution ripples terminate in bounded time via the ripple-ID mechanism. Second, a swarm of about 35 Watchers -- assigned by a verifiable random function, independent of total network size -- suffices for double-spending prevention, matching Hoepman's lower bound. Third, two correct clients can hold conflicting finality attestations only if the adversary compromises a supermajority of the assigned swarm or eclipses both clients from all honest nodes; we prove necessity and sufficiency. Fourth, Buridan's Principle does not apply: the consensus question is absence of evidence, not a binary choice on a continuous input. We also develop a complete economic model. Local coins are issued and retired by currency streams; security weight tracks value automatically as Intercoin weight adjusts at each epoch shuffle. Junior nodes detect corruption and earn lottery rewards for propagating Proofs of Corruption; vesting makes corruption economically irrational. The coin and content layers are strictly separated: regulators observe weight flows without learning amounts, coin types, identities, or rules.