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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
On the Design and Implementation of Structured P2P VPNs
David Isaac Wolinsky, Linton Abraham, Kyungyong Lee, Yonggang Li · 2010-01-15 · via cs.DC updates on arXiv.org

Centralized Virtual Private Networks (VPNs) when used in distributed systems have performance constraints as all traffic must traverse through a central server. In recent years, there has been a paradigm shift towards the use of P2P in VPNs to alleviate pressure placed upon the central server by allowing participants to communicate directly with each other, relegating the server to handling session management and supporting NAT traversal using relays when necessary. Another, less common, approach uses unstructured P2P systems to remove all centralization from the VPN. These approaches currently lack the depth in security options provided by other VPN solutions, and their scalability constraints have not been well studied. In this paper, we propose and implement a novel VPN architecture, which uses a structured P2P system for peer discovery, session management, NAT traversal, and autonomic relay selection and a central server as a partially-automated public key infrastructure (PKI) via a user-friendly web interface. Our model also provides the first design and implementation of a P2P VPN with full tunneling support, whereby all non-P2P based Internet traffic routes through a trusted third party and does so in a way that is more secure than existing full tunnel techniques. To verify our model, we evaluate our reference implementation by comparing it quantitatively to other VPN technologies focusing on latency, bandwidth, and memory usage. We also discuss some of our experiences with developing, maintaining, and deploying a P2P VPN.