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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
Reasonableness discussion and analysis for Hyperledger Fa...
Song Hua, Shenbin Zhang, Bingfeng Pi, Jun Sun, Kazuhiro Yamashit · 2020-05-22 · via cs.DC updates on arXiv.org

Blockchain, as a distributed ledger technology, becomes more and more popular in both industry and academia. Each peer in blockchain system maintains a copy of ledger and makes sure of data consistency through consensus protocol. Blockchain system can provide many benefits such as immutability, transparency and security. Hyperledger Fabric is permissioned blockchain platform hosted by Linux foundation. Fabric has various components such as peer, ordering service, chaincode and state database. The structure of Fabric network is very complicated to provide reliable permissioned blockchain service. Generally, developers must deal with hundreds of parameters to configure a network. That will cause many reasonableness problems in configurations. In this paper, we focus on how to detect reasonableness problems in Fabric configurations. Firstly, we discuss and provide a reasonableness problem knowledge database based on the perspectives of functionality, security and performance. Secondly, we implemented a detect tool for reasonableness check to Fabric. Finally, we collect 108 sample networks as the testing dataset in the experiment. The result shows our tool can help developers to locate reasonableness problems and understand their network better.