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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
Towards Improving the Performance of BFT Consensus For Fu...
Manuel Bravo, Zsolt István, Man-Kit Sit · 2020-07-25 · via cs.DC updates on arXiv.org

Permissioned Blockchains are increasingly considered in enterprise use-cases, many of which do not require geo-distribution, or even disallow it due to legislation. Examples include country-wide networks, such as Alastria, or those deployed using cloud-based platforms such as IBM Blockchain Platform. We expect these blockchains to eventually run in environments with high bandwidth and low latency modern networks, as well as, advanced programmable hardware accelerators in servers. Even though there is renewed interest in BFT consensus algorithms with various proposals targeting Permissioned Blockchains, related work does not optimize for fast networks and does not incorporate hardware accelerators -- we make the case that doing so will pay off in the long run. To this end, we re-implemented the seminal PBFT algorithm in a way that allows us to measure different configurations of the protocol. Through this we explore the benefits of various common optimization strategies and show that the protocol is unlikely to saturate more than 10Gbps networks without relying on specialized hardware-based offloading. We discuss two concrete ways in which the cost of consensus in Permissioned Blockchains could be reduced in high speed networking environments, namely, offloading to SmartNICs and implementing the protocol on standalone FPGAs.