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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
MITOSIS: Practically Scaling Permissioned Blockchains
Giorgia Azzurra Marson, Sebastien Andreina, Lorenzo Alluminio, K · 2021-09-22 · via cs.DC updates on arXiv.org

Scalability remains one of the biggest challenges to the adoption of permissioned blockchain technologies for large-scale deployments. Permissioned blockchains typically exhibit low latencies, compared to permissionless deployments -- however at the cost of poor scalability. Various solutions were proposed to capture "the best of both worlds", targeting low latency and high scalability simultaneously, the most prominent technique being blockchain sharding. However, most existing sharding proposals exploit features of the permissionless model and are therefore restricted to cryptocurrency applications. We present MITOSIS, a novel approach to practically improve scalability of permissioned blockchains. Our system allows the dynamic creation of blockchains, as more participants join the system, to meet practical scalability requirements. Crucially, it enables the division of an existing blockchain (and its participants) into two -- reminiscent of mitosis, the biological process of cell division. MITOSIS inherits the low latency of permissioned blockchains while preserving high throughput via parallel processing. Newly created chains in our system are fully autonomous, can choose their own consensus protocol, and yet they can interact with each other to share information and assets -- meeting high levels of interoperability. We analyse the security of MITOSIS and evaluate experimentally the performance of our solution when instantiated over Hyperledger Fabric. Our results show that MITOSIS can be ported with little modifications and manageable overhead to existing permissioned blockchains, such as Hyperledger Fabric.