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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
A Scalable State Sharing Protocol for Low-Resource Valida...
Ruben Hias, Weihong Wang, Jan Vanhoof, Tom Van Cutsem · 2024-10-08 · via cs.DC updates on arXiv.org

The perpetual growth of data stored on popular blockchains such as Ethereum leads to significant scalability challenges and substantial storage costs for operators of full nodes. Increasing costs may lead to fewer independently operated nodes in the network, which poses risks to decentralization (and hence network security), but also pushes decentralized app developers towards centrally hosted API services. This paper introduces a new protocol that allows validator nodes to participate in a blockchain network without the need to store the full state of the network on each node. The key idea is to use the blockchain network as both a replicated state machine and as a distributed storage system. By distributing states across nodes and enabling efficient data retrieval through a Kademlia-inspired routing protocol, we reduce storage costs for validators. Cryptographic proofs (such as Merkle proofs) are used to allow nodes to verify data stored by other nodes without having to trust those nodes directly. While the protocol trades off data storage for increased network bandwidth, we show how gossiping and caching can minimize the increased bandwidth needs. To validate our state sharing protocol, we conduct an extensive quantitative analysis of Ethereum's data storage and data access patterns. Our findings indicate that while our protocol significantly lowers storage needs, it comes with an increased bandwidth usage ranging from 1.5 MB to 5 MB per block, translating to an additional monthly bandwidth of 319 GB to 1,065 GB. Despite this, the size remains small enough such that it can be passed to all nodes and validated within Ethereum's 12-second block validation window. Further analysis shows that Merkle proofs are the most significant contributor to the additional bandwidth. To address this concern, we also analyze the impact of switching to the more space-efficient Verkle Proofs.