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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
Frosty: Bringing strong liveness guarantees to the Snow f...
Aaron Buchwald, Stephen Buttolph, Andrew Lewis-Pye, Patrick O'Gr · 2024-04-22 · via cs.DC updates on arXiv.org

Snowman is the consensus protocol implemented by the Avalanche blockchain and is part of the Snow family of protocols, first introduced through the original Avalanche leaderless consensus protocol. A major advantage of Snowman is that each consensus decision only requires an expected constant communication overhead per processor in the `common' case that the protocol is not under substantial Byzantine attack, i.e. it provides a solution to the scalability problem which ensures that the expected communication overhead per processor is independent of the total number of processors $n$ during normal operation. This is the key property that would enable a consensus protocol to scale to 10,000 or more independent validators (i.e. processors). On the other hand, the two following concerns have remained: (1) Providing formal proofs of consistency for Snowman has presented a formidable challenge. (2) Liveness attacks exist in the case that a Byzantine adversary controls more than $O(\sqrt{n})$ processors, slowing termination to more than a logarithmic number of steps. In this paper, we address the two issues above. We consider a Byzantine adversary that controls at most $f<n/5$ processors. First, we provide a simple proof of consistency for Snowman. Then we supplement Snowman with a `liveness module' that can be triggered in the case that a substantial adversary launches a liveness attack, and which guarantees liveness in this event by temporarily forgoing the communication complexity advantages of Snowman, but without sacrificing these low communication complexity advantages during normal operation.