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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
Optimistic, Signature-Free Reliable Broadcast and Its App...
Nibesh Shrestha, Qianyu Yu, Aniket Kate, Giuliano Losa, Kartik N · 2025-05-06 · via cs.DC updates on arXiv.org

Reliable broadcast (RBC) is a key primitive in fault-tolerant distributed systems, and improving its efficiency can benefit a wide range of applications. This work focuses on signature-free RBC protocols, which are particularly attractive due to their computational efficiency. Existing protocols in this setting incur an optimal 3 steps to reach a decision while tolerating up to $f < n/3$ Byzantine faults, where $n$ is the number of parties. In this work, we propose an optimistic RBC protocol that maintains the $f < n/3$ fault tolerance but achieves termination in just 2 steps under certain optimistic conditions--when at least $\lceil \frac{n+2f-2}{2} \rceil$ non-broadcaster parties behave honestly. We also prove a matching lower bound on the number of honest parties required for 2-step termination. We show that our latency-reduction technique generalizes beyond RBC and applies to other primitives such as asynchronous verifiable secret sharing (AVSS) and asynchronous verifiable information dispersal (AVID), enabling them to complete in 2 steps under similar optimistic conditions. To highlight the practical impact of our RBC protocol, we integrate it into Sailfish++, a new signature-free, post-quantum secure DAG-based Byzantine fault-tolerant (BFT) consensus protocol. Under optimistic conditions, this protocol achieves a commit latency of 3 steps--matching the performance of the best signature-based protocols. Our experimental evaluation shows that our protocol significantly outperforms existing post-quantum secure and signature-based protocols, even on machines with limited CPU resources. In contrast, signature-based protocols require high CPU capacity to achieve comparable performance. We have open-sourced our Rust implementation of Sailfish++ to facilitate reproducible results.