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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
SurferMonkey: A Decentralized Anonymous Blockchain Interc...
Miguel Díaz Montiel, Rachid Guerraoui, Pierre-Louis Roman · 2022-10-24 · via cs.DC updates on arXiv.org

Blockchain intercommunication systems enable the exchanges of messages between blockchains. This interoperability promotes innovation, unlocks liquidity and access to assets. However, blockchains are isolated systems that originally were not designed for interoperability. This makes cross-chain communication, or bridges for short, insecure by nature. More precisely, cross-chain systems face security challenges in terms of selfish rational players such as maximal extractable value (MEV) and censorship. We propose to solve these challenges using zero knowledge proofs (ZKPs) for cross-chain communication. Securing cross-chain communication is remarkably more complex than securing single-chain events as such a system must preserve user security against both on- and off-chain analysis. To achieve this goal, we propose the following pair of contributions: the DACT protocol and the SurferMonkey infrastructure that supports the DACT protocol. The DACT protocol is a global solution for the anonymity and security challenges of agnostic blockchain intercommunication. DACT breaks on- and off-chain analysis thanks to the use of ZKPs. SurferMonkey is a decentralized infrastructure that implements DACT in practice. Since SurferMonkey works at the blockchain application layer, any decentralized application (dApp) can use SurferMonkey to send any type of message to a dApp on another blockchain. With SurferMonkey, users can neither be censored nor be exposed to MEV. By applying decentralized proactive security, we obtain resilience against selfish rational players, and raise the security bar against cyberattacks. We have implemented a proof of concept (PoC) of SurferMonkey by reverse engineering Tornado Cash and by applying IDEN3 ZKP circuits. SurferMonkey enables new usecases, ranging from anonymous voting and gaming, to a new phase of anonymous decentralized finance (aDeFi).