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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
Technology Review of Blockchain Data Privacy Solutions
Jack Tanner, Roshaan Khan · 2021-05-04 · via cs.DC updates on arXiv.org

This objective of this report is to review existing enterprise blockchain technologies - EOSIO powered systems, Hyperledger Fabric and Besu, Consensus Quorum, R3 Corda and Ernst and Young's Nightfall - that provide data privacy while leveraging the data integrity benefits of blockchain. By reviewing and comparing how and how well these technologies achieve data privacy, a snapshot is captured of the industry's current best practices and data privacy models. Major enterprise technologies are contrasted in parallel to EOSIO to better understand how EOSIO can evolve to meet the trends seen in enterprise blockchain privacy. The following strategies and trends were generally observed in these technologies: Cryptography: the hashing algorithm was found to be the most used cryptographic primitive in enterprise or changeover privacy solutions. Coordination via on-chain contracts - a common strategy was to use a shared publicly ledger to coordinate data privacy groups and more generally managed identities and access control. Transaction and contract code sharing: there was a variety of different levels of privacy around the business logic (smart contract code) visibility. Some solutions only allowed authorised peers to view code while others made this accessible to everybody that was a member of the shared ledger. Data migrations for data privacy applications: significant challenges exist when using cryptographically stored data in terms of being able to run system upgrades. Multiple blockchain ledgers for data privacy: solutions attempted to create a new private blockchain for every private data relationship which was eventually abandoned in favour of one shared ledger with private data collections/transactions that were anchored to the ledger with a hash in order to improve scaling.