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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
Indriya: Building a Secure and Transparent Organ Donation...
Satyajit Ghosh, Mousumi Dutta · 2023-03-07 · via cs.DC updates on arXiv.org

Recent technological advancements have led to the development of new methods for managing organ donation systems, which aim to overcome the limitations of traditional centralized systems. To achieve increased transparency, security, and efficiency in the organ donation process, blockchain technology is being proposed as a replacement for these centralized systems. However, most previous works on organ donation systems have focused on using Ethereum-based blockchain solutions, which offer limited control, a fixed set of consensus protocols, and no support for concurrent executions. In contrast, our work has utilized the Hyperledger Fabric framework to develop a network model of the organ donation system. We have designed and deployed a prototype system with smart contracts using Amazon Managed Blockchain Service. Additionally, we have built a client application that uses the Fabric SDK to interact with the network and perform various actions. To evaluate the performance of our system, we conducted extensive testing using the Hyperledger Caliper benchmarking tool. In our test bench, the system achieved a peak actual send rate of 389.1 transactions per second (TPS) for creating new records and 508.4 TPS for reading records. At a send rate of 800 TPS, the system took an average of 12.16 seconds to serve a request for creating a record and an average of 3.71 seconds to serve a request for reading a record. Future work is required to extend the functionalities of the system and identify potential endorsers and managers for this type of controlled blockchain network.