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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
StableShard: Stable and Scalable Blockchain Sharding with...
Mingzhe Li, You Lin, Jin Zhang · 2024-07-09 · via cs.DC updates on arXiv.org

Sharding enhances blockchain scalability by partitioning nodes into multiple groups for concurrent transaction processing. Configuring a large number of small shards usually helps improve transaction concurrency, but it also increases the fraction of malicious nodes in each shard, easily causing shard corruption and jeopardizing system security. Existing works attempt to improve concurrency by reducing shard sizes while maintaining security, but typically rely on time-consuming recovery of corrupted shards to restore liveness and network-wide consensus. This causes severe system stagnation and limits scalability. To address this, we present StableShard, a sharded blockchain that securely provides high concurrency with stable and scalable performance. The core idea is to carefully co-design the division of labor between proposer shards (PSs) and finalizer committees (FCs): we deliberately assign 1) asymmetric roles and 2) matching parameters to PSs and FCs. Small PSs focus on fast transaction proposal and local validity, while large FCs focus on resolving forks, finalizing PS blocks, and maintaining liveness for faulty PSs via a cross-layer view-change protocol. Moreover, by fine-tuning key system parameters (e.g., shard size, quorum size), we ensure each PS to tolerate <1/2 fraction of malicious nodes without lossing liveness, and allow multiple FCs to securely coexist (each with <1/3 fraction of malicious nodes) for better scalability. Consequently, StableShard can safely configure many smaller PSs to boost concurrency, while FCs and PSs jointly guarantee safety and liveness without system stagnation, leading to stable and scalable performance. Evaluations show that StableShard achieves up to 10x higher throughput than existing solutions and significantly more stable concurrency under attacks.