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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
Secure Consistency Verification for Untrusted Cloud Stora...
Kai Li, Yuzhe Tang, Beom Heyn Kim, Jianliang Xu · 2019-04-14 · via cs.DC updates on arXiv.org

This work presents ContractChecker, a Blockchain-based security protocol for verifying the storage consistency between the mutually distrusting cloud provider and clients. Unlike existing protocols, the ContractChecker uniquely delegates log auditing to the Blockchain, and has the advantages in reducing client cost and lowering requirements on client availability, lending itself to modern scenarios with mobile and web clients. The ContractChecker collects the logs from both clients and the cloud server, and verifies the consistency by cross-checking the logs. By this means, it does not only detects the attacks from malicious clients and server forging their logs, but also is able to mitigate those attacks and recover the system from them. In addition, we design new attacks against ContractChecker exploiting various limits in real Blockchain systems (e.g., write unavailability, Blockchain forks, contract race conditions). We analyze and harden the security of ContractChecker protocols against the proposed new attacks. For evaluating the cost, we build a functional prototype of the ContractChecker on Ethereum/Solidity. By experiments on private and public Ethereum testnets, we extensively evaluate the cost of the ContractChecker in comparison with that of existing client-based log auditing works. The result shows the ContractChecker can scale to hundreds of clients and save client costs by more than one order of magnitude.