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Agentic Performance at the Edge: Insights from Benchmarking Autonomous FAIR Digital Objects: From Passive Assertions to Active Knowledge DP-LAC: Lightweight Adaptive Clipping for Differentially Private Federated Fine-tuning of Language Models Metal-Sci: A Scientific Compute Benchmark for Evolutionary LLM Kernel Search on Apple Silicon From Detection to Recovery: Operational Analysis on LLM Pre-training with 504 GPUs DisagMoE: Computation-Communication overlapped MoE Training via Disaggregated AF-Pipe Parallelism FedGMI: Generative Model-Driven Federated Learning for Probabilistic Mixture Inference PAAC: Privacy-Aware Agentic Device-Cloud Collaboration Transforming the Use of Earth Observation Data: Exascale Training of a Generative Compression Model with Historical Priors for up to 10,000x Data Reduction MARLaaS: Multi-Tenant Asynchronous Reinforcement Learning as a Service FlashEvolve: Accelerating Agent Self-Evolution with Asynchronous Stage Orchestration Private Vertical Federated Inference for Time-Series Dooly: Configuration-Agnostic, Redundancy-Aware Profiling for LLM Inference Simulation FLAM: Evaluating Model Performance with Aggregatable Measures in Federated Learning \mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment SparseRL-Sync: Lossless Weight Synchronization with ~100x Less Communication Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning Execution Envelopes: A Shared Admission Contract for Backend AI Execution Requests Regulating Branch Parallelism in LLM Serving CLAD: A Clustered Label-Agnostic Federated Learning Framework for Joint Anomaly Detection and Attack Classification CCL-Bench 1.0: A Trace-Based Benchmark for LLM Infrastructure Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence VibeServe: Can AI Agents Build Bespoke LLM Serving Systems? 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Multiparty equality in the local broadcast model
Louis Esperet, Jean-Florent Raymond · 2025-10-10 · via cs.DC updates on arXiv.org

In this paper we consider the multiparty equality problem in graphs, where every vertex of a graph $G$ is given an input, and the goal of the vertices is to decide whether all inputs are equal. We study this problem in the local broadcast model, where a message sent by a vertex is received by all its neighbors and the total cost of a protocol is the sum of the lengths of the messages sent by the vertices. This setting was studied by Khan and Vaidya, who gave in 2021 a protocol achieving a 4-approximation in the general case. We study this multiparty communication problem through the lens of network topology. We design a new protocol for 2-connected graphs, whose efficiency relies on the notion of total vertex cover in graph theory. This protocol outperforms the aforementioned 4-approximation in a number of cases. To demonstrate its applicability, we apply it to obtain optimal or asymptotically optimal protocols for several natural network topologies such as cycles, hypercubes, and grids. On the way we also provide new bounds of independent interest on the size of total vertex covers in regular graphs.