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

U
Unit 42
The Cloudflare Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Y
Y Combinator Blog
G
Google Developers Blog
Vercel News
Vercel News
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
Jina AI
Jina AI
Blog — PlanetScale
Blog — PlanetScale
H
Help Net Security
博客园 - 三生石上(FineUI控件)
MongoDB | Blog
MongoDB | Blog
S
SegmentFault 最新的问题
阮一峰的网络日志
阮一峰的网络日志
H
Hackread – Cybersecurity News, Data Breaches, AI and More
aimingoo的专栏
aimingoo的专栏
T
Tailwind CSS Blog
博客园 - 叶小钗
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
月光博客
月光博客
Microsoft Security Blog
Microsoft Security Blog
P
Proofpoint News Feed
The GitHub Blog
The GitHub Blog
云风的 BLOG
云风的 BLOG

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
Iniva: Inclusive and Incentive-compatible Vote Aggregation
Arian Baloochestani, Hanish Gogada, Leander Jehl, Hein Meling · 2024-04-07 · via cs.DC updates on arXiv.org

Many blockchain platforms use committee-based consensus for scalability, finality, and security. In this consensus scheme, a committee decides which blocks get appended to the chain, typically through several voting phases. Platforms typically leverage the committee members' recorded votes to reward, punish, or detect failures. A common approach is to let the block proposer decide which votes to include, opening the door to possible attacks. For example, a malicious proposer can omit votes from targeted committee members, resulting in lost profits and, ultimately, their departure from the system. This paper presents Iniva, an inclusive and incentive-compatible vote aggregation scheme that prevents such vote omission attacks. Iniva relies on a tree overlay with carefully selected fallback paths, making it robust against process failures without needing reconfiguration or additional redundancy. Our analysis shows that Iniva significantly reduces the chance to omit individual votes while ensuring that omitting many votes incurs a significant cost. In addition, our experimental results show that Iniva enjoys robustness, scalability, and reasonable throughput.