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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
Superlight -- A Permissionless, Light-client Only Blockch...
Roman Blum, Thomas Bocek · 2019-01-08 · via cs.DC updates on arXiv.org

Blockchain protocols are based on a distributed database where stored data is guaranteed to be immutable. The requirement that all nodes have to maintain their own local copy of the database ensures security while consensus mechanisms help deciding which data gets added to the database and keep powerful adversaries from derailing the system. However, since the database that forms the foundation of a blockchain is a continuously growing list of blocks, scalability is an inherent problem of this technology. Some public blockchains need a few 100 GB to Terabytes of storage. In this work, we present the concept Superlight with self-contained proofs, which is designed to improve scalability of a public blockchain, while preserving security and decentralization. Instead of all nodes having a local copy of the whole blockchain to verify a transaction, nodes can derive the validity of a transaction by only using block headers. To keep the block headers compact, BLS signatures are used to combine signatures. We provide a definition of SCPs and show the required steps of a client to create a proof that is accepted by other nodes for transferring funds. The advantage of such a light-client-only blockchain is the lower storage requirement, while the drawback is an increased computational complexity due to BLS signatures, limited use-cases due to lack of a global state, and the requirement for an interactive protocol between sender, receiver, and miner to create a transaction.