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Welcome NVIDIA Cosmos 3: The First Open Omni-model for Physical AI Reasoning and Action6h◆Physically Viable World Models: A Case for Query-Conditioned Embodied AI7h◆Discovering a Zeta Map Algorithm on Dyck Paths via Mechanistic Interpretability7h◆Diagnosing Failure Modes of Shared-State Collaboration in Resource-Constrained Visual Agents7h◆Answer-Set-Programming-based Abstractions for Reinforcement Learning7h◆TRINE: A Token-Aware, Runtime-Adaptive FPGA Inference Engine for Multimodal AI7h◆Prior Availability in Industrial Visual Sim-to-Real: A Review of CAD-Guided and CAD-Unavailable Regimes7h◆BOKBO (Best of K Bad Options): Calibrated Abstention for VLA Policies7h◆Universal Decision Learners7h◆Algorithmic Recourse of In-Context Learning for Tabular Data7h◆Graph Machine Learning in the Era of Large Language Models (LLMs)7h◆NGDBench: Towards Neural Graph Data Management7h◆End-to-End Compression for Tabular Foundation Models7h◆SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning7h◆A Pilot Study on Curator-Guided Multilingual Art Description for Blind and Low-Vision Audiences with Small Vision-Language Models7h◆Memory by Design: Probabilistic Sequence Layers7h◆The Terminal Representation in Reinforcement Learning7h◆Counterfactual Trace Auditing of LLM Agent Skills7h◆GETA: Generalized Encrypted Traffic Analysis7h◆SERA: Soft-Verified Efficient Repository Agents7h◆Welcome NVIDIA Cosmos 3: The First Open Omni-model for Physical AI Reasoning and Action6h◆Physically Viable World Models: A Case for Query-Conditioned Embodied AI7h◆Discovering a Zeta Map Algorithm on Dyck Paths via Mechanistic Interpretability7h◆Diagnosing Failure Modes of Shared-State Collaboration in Resource-Constrained Visual Agents7h◆Answer-Set-Programming-based Abstractions for Reinforcement Learning7h◆TRINE: A Token-Aware, Runtime-Adaptive FPGA Inference Engine for Multimodal AI7h◆Prior Availability in Industrial Visual Sim-to-Real: A Review of CAD-Guided and CAD-Unavailable Regimes7h◆BOKBO (Best of K Bad Options): Calibrated Abstention for VLA Policies7h◆Universal Decision Learners7h◆Algorithmic Recourse of In-Context Learning for Tabular Data7h◆Graph Machine Learning in the Era of Large Language Models (LLMs)7h◆NGDBench: Towards Neural Graph Data Management7h◆End-to-End Compression for Tabular Foundation Models7h◆SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning7h◆A Pilot Study on Curator-Guided Multilingual Art Description for Blind and Low-Vision Audiences with Small Vision-Language Models7h◆Memory by Design: Probabilistic Sequence Layers7h◆The Terminal Representation in Reinforcement Learning7h◆Counterfactual Trace Auditing of LLM Agent Skills7h◆GETA: Generalized Encrypted Traffic Analysis7h◆SERA: Soft-Verified Efficient Repository Agents7h◆
News/The Gaussian-Head OFL Family: One-Shot Federated Learning from Client Global Statistics
arxiv
PublishedJune 1, 2026 at 4:00 AM

The Gaussian-Head OFL Family: One-Shot Federated Learning from Client Global Statistics

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arXiv:2602.01186v2 Announce Type: replace-cross Abstract: Classical Federated Learning relies on a multi-round iterative process of model exchange and aggregation between server and clients, with high communication costs and privacy risks from repeated model transmissions. In contrast, one-shot fede

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