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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
Trinity: A Distributed Publish/Subscribe Broker with Bloc...
Gowri Sankar Ramachandran, Kwame-Lante Wright, Bhaskar Krishnama · 2018-06-12 · via cs.DC updates on arXiv.org

Internet-of-Things (IoT) and Supply Chain monitoring applications rely on messaging protocols for exchanging data. Contemporary IoT deployments widely use the publish-subscribe messaging model because of its resource-efficiency. However, the systems with publish-subscribe messaging model employ a centralized architecture, wherein the data from all the devices in the application network flows via a central broker to the subscribers. Such a centralized architecture make publish-subscribe messaging model susceptible to a central point of failure. Besides, it provides an opportunity for the organization that owns the broker to tamper with the data. In this work, we contribute Trinity, a novel distributed publish-subscribe broker with blockchain-based immutability. Trinity distributes the data published to one of the brokers in the network to all the brokers in the network. The distributed data is stored in an immutable ledger through the use of the blockchain technology. Furthermore, Trinity executes smart contracts to validate the data before saving the data on the blockchain. Through the use of a blockchain network, Trinity can guarantee persistence, ordering, and immutability across trust boundaries. Our evaluation results show that Trinity consumes minimal resources, and the use of smart contracts enable the stakeholders to automate the data management processes. To the best of our knowledge, Trinity is the first framework that combines the components of the blockchain technology with the publish-subscribe messaging model.