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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? 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On the Practicality of `Practical' Byzantine Fault Tolerance
Nikos Chondros, Konstantinos Kokordelis, Mema Roussopoulos · 2011-10-22 · via cs.DC updates on arXiv.org

Byzantine Fault Tolerant (BFT) systems are considered by the systems research community to be state of the art with regards to providing reliability in distributed systems. BFT systems provide safety and liveness guarantees with reasonable assumptions, amongst a set of nodes where at most f nodes display arbitrarily incorrect behaviors, known as Byzantine faults. Despite this, BFT systems are still rarely used in practice. In this paper we describe our experience, from an application developer's perspective, trying to leverage the publicly available and highly-tuned PBFT middleware (by Castro and Liskov), to provide provable reliability guarantees for an electronic voting application with high security and robustness needs. We describe several obstacles we encountered and drawbacks we identified in the PBFT approach. These include some that we tackled, such as lack of support for dynamic client management and leaving state management completely up to the application. Others still remaining include the lack of robust handling of non-determinism, lack of support for web-based applications, lack of support for stronger cryptographic primitives, and others. We find that, while many of the obstacles could be overcome with a revised BFT middleware implementation that is tuned specifically for the needs of the particular application, they require significant engineering effort and time and their performance implications for the end-application are unclear. An application developer is thus unlikely to be willing to invest the time and effort to do so to leverage the BFT approach. We conclude that the research community needs to focus on the usability of BFT algorithms for real world applications, from the end-developer perspective, in addition to continuing to improve the BFT middleware performance, robustness and deployment layouts.