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Welcome NVIDIA Cosmos 3: The First Open Omni-model for Physical AI Reasoning and Action4h◆Physically Viable World Models: A Case for Query-Conditioned Embodied AI5h◆Discovering a Zeta Map Algorithm on Dyck Paths via Mechanistic Interpretability5h◆Diagnosing Failure Modes of Shared-State Collaboration in Resource-Constrained Visual Agents5h◆Answer-Set-Programming-based Abstractions for Reinforcement Learning5h◆TRINE: A Token-Aware, Runtime-Adaptive FPGA Inference Engine for Multimodal AI5h◆Prior Availability in Industrial Visual Sim-to-Real: A Review of CAD-Guided and CAD-Unavailable Regimes5h◆BOKBO (Best of K Bad Options): Calibrated Abstention for VLA Policies5h◆Universal Decision Learners5h◆Algorithmic Recourse of In-Context Learning for Tabular Data5h◆Graph Machine Learning in the Era of Large Language Models (LLMs)5h◆NGDBench: Towards Neural Graph Data Management5h◆Autoregressive Visual Generation Needs a Prologue5h◆Learning Randomized Reductions5h◆Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles5h◆dgMARK: Decoding-Guided Watermarking for Diffusion Language Models5h◆End-to-End Compression for Tabular Foundation Models5h◆The Fundamental Limits of Fraud Detection in Card Payment Networks5h◆Free energy Estimation on Any State Space5h◆View Space: Learning Representation across Arbitrary Graphs5h◆Welcome NVIDIA Cosmos 3: The First Open Omni-model for Physical AI Reasoning and Action4h◆Physically Viable World Models: A Case for Query-Conditioned Embodied AI5h◆Discovering a Zeta Map Algorithm on Dyck Paths via Mechanistic Interpretability5h◆Diagnosing Failure Modes of Shared-State Collaboration in Resource-Constrained Visual Agents5h◆Answer-Set-Programming-based Abstractions for Reinforcement Learning5h◆TRINE: A Token-Aware, Runtime-Adaptive FPGA Inference Engine for Multimodal AI5h◆Prior Availability in Industrial Visual Sim-to-Real: A Review of CAD-Guided and CAD-Unavailable Regimes5h◆BOKBO (Best of K Bad Options): Calibrated Abstention for VLA Policies5h◆Universal Decision Learners5h◆Algorithmic Recourse of In-Context Learning for Tabular Data5h◆Graph Machine Learning in the Era of Large Language Models (LLMs)5h◆NGDBench: Towards Neural Graph Data Management5h◆Autoregressive Visual Generation Needs a Prologue5h◆Learning Randomized Reductions5h◆Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles5h◆dgMARK: Decoding-Guided Watermarking for Diffusion Language Models5h◆End-to-End Compression for Tabular Foundation Models5h◆The Fundamental Limits of Fraud Detection in Card Payment Networks5h◆Free energy Estimation on Any State Space5h◆View Space: Learning Representation across Arbitrary Graphs5h◆
News/Separate Aggregation of Split Network for Personalized Federated Learning
arxiv
PublishedMay 27, 2026 at 4:00 AM
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Separate Aggregation of Split Network for Personalized Federated Learning

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arXiv:2605.26571v1 Announce Type: new Abstract: Federated learning enables collaborative model training without sharing raw data, but its performance can degrade substantially under heterogeneous client data distributions. A single global model often cannot satisfy diverse client requirements, so pe

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