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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
Possibility and Impossibility of Reliable Broadcast in th...
Danny Dolev, Meir Spielrien · 2016-11-16 · via cs.DC updates on arXiv.org

The Reliable Broadcast concept allows an honest party to send a message to all other parties and to make sure that all honest parties receive this message. In addition, it allows an honest party that received a message to know that all other honest parties would also receive the same message. This technique is important to ensure distributed consistency when facing failures. In the current paper, we study the ability to use \RR to consistently transmit a sequence of input values in an asynchronous environment with a designated sender. The task can be easily achieved using counters, but cannot be achieved with a bounded memory facing failures. We weaken the problem and ask whether the receivers can at least share a common suffix. We prove that in a standard (lossless) asynchronous system no bounded memory protocol can guarantee a common suffix at all receivers for every input sequence if a single party might crash. We further study the problem facing transient faults and prove that when limiting the problem to transmitting a stream of a single value being sent repeatedly we show a bounded memory self-stabilizing protocol that can ensure a common suffix even in the presence of transient faults and an arbitrary number of crash faults. We further prove that this last problem is not solvable in the presence of a single Byzantine fault. Thus, this problem {\bf separates} Byzantine behavior from crash faults in an asynchronous environment.