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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
SPEEDEX: A Scalable, Parallelizable, and Economically Eff...
Geoffrey Ramseyer, Ashish Goel, David Mazières · 2021-11-04 · via cs.DC updates on arXiv.org

SPEEDEX is a decentralized exchange (DEX) that lets participants securely trade assets without giving any single party undue control over the market. SPEEDEX offers several advantages over prior DEXes. It achieves high throughput -- over 200,000 transactions per second on 48-core servers, even with tens of millions of open offers. SPEEDEX runs entirely within a Layer-1 blockchain, and thus achieves its scalability without fragmenting market liquidity between multiple blockchains or rollups. It eliminates internal arbitrage opportunities, so that a direct trade from asset $\mathcal{A}$ to asset $\mathcal{B}$ always receives as good a price as trading through some third asset such as USD. Finally, it prevents certain front-running attacks that would otherwise increase the effective bid-ask spread for small traders. SPEEDEX's key design insight is its use of an Arrow-Debreu exchange market structure that fixes the valuation of assets for all trades in a given block of transactions. We construct an algorithm, which is both asymptotically efficient and empirically practical, that computes these valuations while exactly preserving a DEX's financial correctness constraints. Not only does this market structure provide fairness across trades, but it also makes trade operations commutative and hence efficiently parallelizable. SPEEDEX is prototyped but not yet merged within the Stellar blockchain, one of the largest Layer-1 blockchains.