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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
A DLT-based Smart Contract Architecture for Atomic and Sc...
J. Kalbantner, K. Markantonakis, D. Hurley-Smith, C. Shepherd, B · 2021-05-07 · via cs.DC updates on arXiv.org

Distributed Ledger Technology (DLT) has an enormous potential but also downsides. One downside of many DLT systems, such as blockchain, is their limited transaction throughput that hinders their adoption in many use cases (e.g., real-time payments). State channels have emerged as a potential solution to enhance throughput by allowing transactions to process off-chain. While current proposals can increase scalability, they require high collateral and lack support for dynamic systems that require asynchronous state transitions. Additionally, the latency of channel initialisations can cause issues especially if fast interactions are required. In this paper, we propose an atomic, scalable and privacy-preserving protocol that enables secure and dynamic updates. We develop a smart contract-based Credit-Note System (CNS) that allows participants to lock funds before a state channel initialisation, which enhances flexibility and efficiency. We formalise our model using the Universal Composability (UC) framework and demonstrate that it achieves the stated design goals of privacy, scalability, and atomicity. Moreover, we implement a dispute process in the state channel to counter availability attacks. Finally, we analyse the protocol in the context of an asynchronous smart grid-based marketplace.