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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
Flow: Separating Consensus and Compute -- Block Formation...
Alexander Hentschel, Yahya Hassanzadeh-Nazarabadi, Ramtin Seraj, · 2020-02-18 · via cs.DC updates on arXiv.org

Most current blockchains require all full nodes to execute all tasks limits the throughput of existing blockchains, which are well documented and among the most significant hurdles for the widespread adoption of decentralized technology. This paper extends out presentation of Flow, a pipelined blockchain architecture, which separates the process of consensus on the transaction order from transaction computation. As we experimentally showed in our previous white paper, our architecture provides a significant throughput improvement while preserving the security of the system. Flow exploits the heterogeneity offered by the nodes, in terms of bandwidth, storage, and computational capacity, and defines the roles for the nodes based on their tasks in the pipeline, i.e., Collector, Consensus, Execution, and Verification. While transaction collection from the user agents is completed through the bandwidth-optimized Collector Nodes, the execution of them is done by the compute-optimized Execution Nodes. Checking the execution result is then distributed among a more extensive set of Verification Nodes, which confirm the result is correct in a distributed and parallel manner. In contrast to more traditional blockchain architectures, Flow's Consensus Nodes do not execute the transaction. Instead, Verification Nodes report observed faulty executions to the Consensus Nodes, which adjudicate the received challenges and slash malicious actors. In this paper, we detail the lifecycle of the transactions from the submission to the system until they are getting executed. The paper covers the Collector, Consensus, and Execution role. We provide a protocol specification of collecting the transactions, forming a block, and executing the resulting block. Moreover, we elaborate on the safety and liveness of the system concerning these processes.