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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
Consensus Algorithms of Distributed Ledger Technology -- ...
Ahmad J. Alkhodair, Saraju P. Mohanty, Elias Kougianos · 2023-09-24 · via cs.DC updates on arXiv.org

The most essential component of every Distributed Ledger Technology (DLT) is the Consensus Algorithm (CA), which enables users to reach a consensus in a decentralized and distributed manner. Numerous CA exist, but their viability for particular applications varies, making their trade-offs a crucial factor to consider when implementing DLT in a specific field. This article provided a comprehensive analysis of the various consensus algorithms used in distributed ledger technologies (DLT) and blockchain networks. We cover an extensive array of thirty consensus algorithms. Eleven attributes including hardware requirements, pre-trust level, tolerance level, and more, were used to generate a series of comparison tables evaluating these consensus algorithms. In addition, we discuss DLT classifications, the categories of certain consensus algorithms, and provide examples of authentication-focused and data-storage-focused DLTs. In addition, we analyze the pros and cons of particular consensus algorithms, such as Nominated Proof of Stake (NPoS), Bonded Proof of Stake (BPoS), and Avalanche. In conclusion, we discuss the applicability of these consensus algorithms to various Cyber Physical System (CPS) use cases, including supply chain management, intelligent transportation systems, and smart healthcare.