Showing 1–20 of 28 results
/ Date/ Name
Nov 23, 2016A Truthful $(1-ε)$-Optimal Mechanism for On-demand Cloud Resource ProvisioningMar 12, 2020Machine Learning on Volatile InstancesApr 23, 2025Conditional-Marginal Nonparametric Estimation for Stage Waiting Times from Multi-Stage Models under Dependent Right CensoringNov 21, 2019Observe Before Play: Multi-armed Bandit with Pre-observationsJan 16, 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge AssociationApr 22, 2023Towards Carbon-Neutral Edge Computing: Greening Edge AI by Harnessing Spot and Future Carbon MarketsOct 25, 2024In-architecture X-ray assisted C-Br dissociation for on-surface fabrication of diamondoid chainsOct 20, 2023DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming DatasetAug 18, 2025Accelerating Edge Inference for Distributed MoE Models with Latency-Optimized Expert PlacementJun 12, 2020Towards Flexible Device Participation in Federated LearningNov 4, 2024FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant ClientsJan 12, 2025Real-Time Neural-Enhancement for Online Cloud GamingApr 24, 2025TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive CorrectionNov 4, 2024FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained AggregationJul 30, 2024Thermal spin-crossover and temperature-dependent zero-field splitting in magnetic nanographene chainsDec 29, 2025Certifying the Right to Be Forgotten: Primal-Dual Optimization for Sample and Label Unlearning in Vertical Federated LearningJul 19, 2023A3D: Adaptive, Accurate, and Autonomous Navigation for Edge-Assisted DronesJul 4, 2023Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT ServicesApr 11, 2026Verifying In-Network Computing Systems for Design RisksFeb 1, 2023Task Placement and Resource Allocation for Edge Machine Learning: A GNN-based Multi-Agent Reinforcement Learning Paradigm