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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
Blockchain-based Trust Management in Security Credential ...
SangHyun Byun, Arijet Sarker, Sang-Yoon Chang, Jugal Kalita · 2025-02-22 · via cs.DC updates on arXiv.org

Cellular networking is advancing as a wireless technology to support diverse applications in vehicular communication, enabling vehicles to interact with various applications to enhance the driving experience, even when managed by different authorities. Security Credential Management System (SCMS) is the Public Key Infrastructure (PKI) for vehicular networking and the state-of-the-art distributed PKI to protect the privacy-preserving vehicular networking against an honest-but-curious authority using multiple authorities and to decentralize the trust management. We build a Blockchain-Based Trust Management (BBTM) to provide even greater decentralization and security. Specifically, BBTM uses the blockchain to 1) replace the existing Policy Generator (PG), 2) manage the policy of each authority in SCMS, 3) aggregate the Global Certificate Chain File (GCCF), and 4) provide greater accountability and transparency on the aforementioned functionalities. We implement BBTM on Hyperledger Fabric using a smart contract for experimentation and analyses. Our experiments show that BBTM is lightweight in processing, efficient management in the certificate chain and ledger size, supports a bandwidth of multiple transactions per second, and provides validated end-entities.