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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
Perfectly-Secure Synchronous MPC with Asynchronous Fallba...
Ananya Appan, Anirudh Chandramouli, Ashish Choudhury · 2022-01-28 · via cs.DC updates on arXiv.org

Secure multi-party computation (MPC) is a fundamental problem in secure distributed computing. An MPC protocol allows a set of $n$ mutually distrusting parties to carry out any joint computation of their private inputs, without disclosing any additional information about their inputs. MPC with information-theoretic security provides the strongest security guarantees and remains secure even against computationally unbounded adversaries. Perfectly-secure MPC protocols is a class of information-theoretically secure MPC protocols, which provides all the security guarantees in an error-free fashion. The focus of this work is perfectly-secure MPC. Known protocols are designed assuming either a synchronous or asynchronous communication network. It is well known that perfectly-secure synchronous MPC protocol is possible as long as adversary can corrupt any $t_s < n/3$ parties. On the other hand, perfectly-secure asynchronous MPC protocol can tolerate up to $t_a < n/4$ corrupt parties. A natural question is does there exist a single MPC protocol for the setting where the parties are not aware of the exact network type and which can tolerate up to $t_s < n/3$ corruptions in a synchronous network and up to $t_a < n/4$ corruptions in an asynchronous network. We design such a best-of-both-worlds perfectly-secure MPC protocol, provided $3t_s + t_a < n$ holds. For designing our protocol, we design two important building blocks, which are of independent interest. The first building block is a best-of-both-worlds Byzantine agreement (BA) protocol tolerating $t < n/3$ corruptions and which remains secure, both in a synchronous as well as asynchronous network. The second building block is a polynomial-based best-of-both-worlds verifiable secret-sharing (VSS) protocol, which can tolerate up to $t_s$ and $t_a$ corruptions in a synchronous and in an asynchronous network respectively.