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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
GeoShield: Byzantine Fault Detection and Recovery for Geo...
Yifan Cai, Linh Thi Xuan Phan · 2025-11-19 · via cs.DC updates on arXiv.org

Large-scale cyber-physical systems (CPS), such as railway control systems and smart grids, consist of geographically distributed subsystems that are connected via unreliable, asynchronous inter-region networks. Their scale and distribution make them especially vulnerable to faults and attacks. Unfortunately, existing fault-tolerant methods either consume excessive resources or provide only eventual guarantees, making them unsuitable for real-time resource-constrained CPS. We present GeoShield, a resource-efficient solution for defending geo-distributed CPS against Byzantine faults. GeoShield leverages the property that CPS are designed to tolerate brief disruptions and maintain safety, as long as they recover (i.e., resume normal operations or transition to a safe mode) within a bounded amount of time following a fault. Instead of masking faults, it detects them and recovers the system within bounded time, thus guaranteeing safety with much fewer resources. GeoShield introduces protocols for Byzantine fault-resilient network measurement and inter-region omission fault detection that proactively detect malicious message delays, along with recovery mechanisms that guarantee timely recovery while maximizing operational robustness. It is the first bounded-time recovery solution that operates effectively under unreliable networks without relying on trusted hardware. Evaluations using real-world case studies show that it significantly outperforms existing methods in both effectiveness and resource efficiency.