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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
Designing Asynchronous Multiparty Protocols with Crash-St...
Adam D. Barwell, Ping Hou, Nobuko Yoshida, Fangyi Zhou · 2023-05-10 · via cs.DC updates on arXiv.org

Session types provide a typing discipline for message-passing systems. However, most session type approaches assume an ideal world: one in which everything is reliable and without failures. Yet this is in stark contrast with distributed systems in the real world. To address this limitation, we introduce Teatrino, a code generation toolchain that utilises asynchronous multiparty session types (MPST) with crash-stop semantics to support failure handling protocols. We augment asynchronous MPST and processes with crash handling branches. Our approach requires no user-level syntax extensions for global types and features a formalisation of global semantics, which captures complex behaviours induced by crashed/crash handling processes. The sound and complete correspondence between global and local type semantics guarantees deadlock-freedom, protocol conformance, and liveness of typed processes in the presence of crashes. Our theory is implemented in the toolchain Teatrino, which provides correctness by construction. Teatrino extends the Scribble multiparty protocol language to generate protocol-conforming Scala code, using the Effpi concurrent programming library. We extend both Scribble and Effpi to support crash-stop behaviour. We demonstrate the feasibility of our methodology and evaluate Teatrino with examples extended from both session type and distributed systems literature.