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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
Coded Transaction Broadcasting for High-throughput Blockc...
Lei Yang, Yossi Gilad, Mohammad Alizadeh · 2022-05-04 · via cs.DC updates on arXiv.org

High-throughput blockchains require efficient transaction broadcast mechanisms that can deliver transactions to most network nodes with low bandwidth overhead and latency. Existing schemes coordinate transmissions across peers to avoid sending redundant data, but they either incur a high latency or are not robust against adversarial network nodes. We present Strokkur, a new transaction broadcasting mechanism that provides both low bandwidth overhead and low latency. The core idea behind Strokkur is to avoid explicit coordination through randomized transaction coding. Rather than forward individual transactions. Strokkur nodes send out codewords -- XOR sums of multiple transactions selected at random. Since almost every codeword is useful for the receiver to decode new transactions, Strokkur nodes do not require coordination, for example, to determine which transactions the receiver is missing. Strokkur's coding strategy builds on LT codes, a popular class of rateless erasure codes, and extends them to support multiple uncoordinated senders with partially-overlapping continual streams of transaction data. Strokkur introduces mechanisms to cope with adversarial senders that may send corrupt codewords, and a simple rate control algorithm that enables each node to independently determine an appropriate sending rate of codewords for each peer. Our implementation of Strokkur in Golang supports 647k transactions per second using only one CPU core. Our evaluation across a 19-node Internet deployment and large-scale simulation show that Strokkur consumes 2--7.6x less bandwidth than the existing scheme in Bitcoin, and 9x lower latency that Shrec when only 4% of nodes are adversarial.