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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
Byzantine Processors and Cuckoo Birds: Confining Maliciou...
Danny Dolev, Eli Gafni · 2016-11-14 · via cs.DC updates on arXiv.org

Are there Byzantine Animals? A Fooling Behavior is exhibited by the Cuckoo bird. It sneakily replaces some of the eggs of other species with its own. Lest the Cuckoo extinct itself by destroying its host, it self-limits its power: It does not replace too large a fraction of the eggs. Here, we show that any Byzantine Behavior that does not destroy the system it attacks, i.e. allows the system to solve an easy task like epsilon-agreement, then its maliciousness can be confined to be the exact replica of the Cuckoo bird behavior: Undetectably replace an input of a processor and let the processor behave correctly thereafter with respect to the new input. In doing so we reduce the study of Byzantine behavior to fail-stop (benign) behavior with the Cuckoo caveat of a fraction of the inputs replaced. We establish a complete correspondence between the Byzantine and the Benign, modulo different thresholds, and replaced inputs. This work is yet another step in a line of work unifying seemingly distinct distributed system models, dispelling the Myth that Distributed Computing is a plethora of distinct isolated models, each requiring its specialized tools and ideas in order to determine solvability of tasks. Thus, hereafter, Byzantine Computability questions can be reduced to questions in the benign failure setting. We also show that the known results about correlated faults in the asynchronous benign setting can be imported verbatim to the asynchronous Byzantine setting. Finally, as in the benign case in which we have the property that a processor can output once its faulty behavior stops for long enough, we show this can be done in a similar manner in the Byzantine case. This necessitated the generalization of Reliable Broadcast to what we term Recoverable Reliable Broadcast.