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

Stack Overflow Blog
Stack Overflow Blog
T
Tailwind CSS Blog
Recent Announcements
Recent Announcements
宝玉的分享
宝玉的分享
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
P
Proofpoint News Feed
D
Docker
Google DeepMind News
Google DeepMind News
aimingoo的专栏
aimingoo的专栏
B
Blog RSS Feed
Microsoft Security Blog
Microsoft Security Blog
博客园 - 【当耐特】
M
MIT News - Artificial intelligence
云风的 BLOG
云风的 BLOG
小众软件
小众软件
Hugging Face - Blog
Hugging Face - Blog
WordPress大学
WordPress大学
IT之家
IT之家
H
Help Net Security
Apple Machine Learning Research
Apple Machine Learning Research
Martin Fowler
Martin Fowler
S
SegmentFault 最新的问题
B
Blog
D
DataBreaches.Net

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
Witnet: A Decentralized Oracle Network Protocol
Adán Sánchez de Pedro, Daniele Levi, Luis Iván Cuende · 2017-11-27 · via cs.DC updates on arXiv.org

Witnet is a decentralized oracle network (DON) that connects smart contracts to the outer world. Generally speaking, it allows any piece of software to retrieve the contents published at any web address at a certain point in time, with complete and verifiable proof of its integrity and without blindly trusting any third party. Witnet runs on a blockchain with a native protocol token (called Wit), which miners-called witnesses-earn by retrieving, attesting and delivering web contents for clients. On the other hand, clients spend Wit to pay witnesses for their Retrieve-Attest-Deliver (RAD) work. Witnesses also compete to mine blocks with considerable rewards, but Witnet mining power is proportional to their previous performance in terms of honesty and trustworthiness-this is, their reputation as witnesses. This creates a powerful incentive for witnesses to do their work honestly, protect their reputation and not to deceive the network. The Witnet protocol is designed to assign the RAD tasks to witnesses in a way that mitigates most attack vectors to the greatest extent. At the same time, it includes a novel 'sharding' feature that (1) guarantees the efficiency and scalability of the network, (2) keeps the price of RAD tasks within reasonable bounds and (3) gives clients the freedom to adjust certainty and price by letting them choose how many witnesses will work on their RAD tasks. When coupled with a Decentralized Storage Network (DSN), Witnet also gives us the possibility to build the Digital Knowledge Ark: a decentralized, immutable, censorship-resistant and eternal archive of humanity's most relevant digital data. A truth vault aimed to ensure that knowledge will remain democratic and verifiable forever and to prevent history from being written by the victors.