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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
XPC: Fast and Reliable Synchronous Transmission Protocols...
Alberto Spina, Michael Breza, Naranker Dulay, Julie McCann · 2019-10-22 · via cs.DC updates on arXiv.org

One of the major challenges for the engineering of wireless sensing systems is to improve the software abstractions and frameworks that are available to programmers while ensuring system reliability and efficiency. The distributed systems community have developed a rich set of such abstractions for building dependable distributed systems connected using wired networks, however after 20 years research many of these elude wireless sensor systems. In this paper we present X Process Commit (XPC) an atomic commit protocol framework that utilizes Synchronous Transmission (ST). We also introduce Hybrid, a technique that allows us to exploit the advantages of the Glossy and Chaos Synchronous Transmission primitives to get lower latency and higher reliability than either. Using XPC and Hybrid we demonstrate how to build protocols for the classical 2-phase and 3-phase commit abstractions and evaluate these demonstrating significantly improved performance and reliability than the use of Glossy or Chaos individually as dissemination primitives. We address how we overcame the timing challenges of bringing Glossy and Chaos together to form Hybrid and through extensive experimentation demonstrate that it is robust to in-network radio interference caused by multiple sources. We are first to present testbed results that show that Hybrid can provide almost 100% reliability in a network of nodes suffering from various levels of radio interference.