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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
SOK: Blockchain for Provenance
Asma Jodeiri Akbarfam, Hoda Maleki · 2024-07-25 · via cs.DC updates on arXiv.org

Provenance, which traces data from its creation to manipulation, is crucial for ensuring data integrity, reliability, and trustworthiness. It is valuable for single-user applications, collaboration within organizations, and across organizations. Blockchain technology has become a popular choice for implementing provenance due to its distributed, transparent, and immutable nature. Numerous studies on blockchain designs are specifically dedicated to provenance, and specialize in this area. Our goal is to provide a new perspective in blockchain based provenance field by identifying the challenges faced and suggesting future research directions. In this paper, we categorize the problem statement into three main research questions to investigate key issues comprehensively and propose a new outlook on the use of blockchains. The first focuses on challenges in non-collaborative, single-source environments, the second examines implications in collaborative environments and different domains such as supply chain, scientific collaboration and digital forensic, and the last one analyzes communication and data exchange challenges between organizations using different blockchains. The interconnected nature of these research questions ensures a thorough exploration of provenance requirements, leading to more effective and secure systems. After analyzing the requirements of provenance in different environments, we provide future design considerations for provenance-based blockchains, including blockchain type, query mechanisms, provenance capture methods, and domain-specific considerations. We also discuss future work and possible extensions in this field.