Statistical Model Aggregation via Parameter Matching
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Abstract (arXiv)
We consider the problem of aggregating models learned from sequestered, possibly heterogeneous datasets. Exploiting tools from Bayesian nonparametrics, we develop a general meta-modeling framework that learns shared global latent structures by identifying correspondences among local model parameterizations. Our proposed framework is model-independent and is applicable to a wide range of model types. After verifying our approach on simulated data, we demonstrate its utility in aggregating Gaussian topic models, hierarchical Dirichlet process based hidden Markov models, and sparse Gaussian processes with applications spanning text summarization, motion capture analysis, and temperature forecasting.
Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Nov 2019.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 7; 2024: 0; 2025: 3; 2026: 3.
17 papers.
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- 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
- 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
- 2023Efficient Personalized Federated Learning via Sparse Model-Adaptationcited by 38
- 2022Federated Learning with Position-Aware Neuronscited by 24
- 2021An Expectation-Maximization Perspective on Federated Learningcited by 9
- 2025Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Datacited by 9
- 2023Federated Learning of Models Pre-Trained on Different Features with Consensus Graphscited by 8
- 2025Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Datacited by 7
- 2025Optimal Transport Depth Up-Scalingcited by 4
- 2023Model-Agnostic Federated Learningcited by 3
- 2023Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectivescited by 3
- 2026Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Datacited by 3
5 more
- 2022Bayesian Federated Neural Matching that Completes Full Informationcited by 1
- 2022Investigating Neuron Disturbing in Fusing Heterogeneous Neural Networkscited by 0
- 2023The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learningcited by 0
- 2026Model Merging as Probabilistic Inference in Fine-Tuning Parameter Spacecited by 0
- 2026Federated Clustering with Unknown Local and Global Cluster Cardinalitiescited by 0
- 2026Federated Clustering with Unknown Local and Global Cluster Cardinalities29 Sep 2026
- 2026Model Merging as Probabilistic Inference in Fine-Tuning Parameter Space2 Jul 2026
- 2026Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Data6 Feb 2026
- 2025Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Data27 Oct 2025
- 2025Optimal Transport Depth Up-Scaling11 Aug 2025
- 2025Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data27 Feb 2025
- 2023The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning5 Dec 2023
- 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark12 Nov 2023
- 2023Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives5 Jul 2023
- 2023Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs2 Jun 2023
- 2023Efficient Personalized Federated Learning via Sparse Model-Adaptation4 May 2023
- 2023Model-Agnostic Federated Learning8 Mar 2023
5 more
- 2023Recent Advances on Federated Learning: A Systematic Survey3 Jan 2023
- 2022Bayesian Federated Neural Matching that Completes Full Information15 Nov 2022
- 2022Investigating Neuron Disturbing in Fusing Heterogeneous Neural Networks24 Oct 2022
- 2022Federated Learning with Position-Aware Neurons28 Mar 2022
- 2021An Expectation-Maximization Perspective on Federated Learning19 Nov 2021
- 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
- 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
- 2023Efficient Personalized Federated Learning via Sparse Model-Adaptationcited by 38
- 2022Federated Learning with Position-Aware Neuronscited by 24
- 2021An Expectation-Maximization Perspective on Federated Learningcited by 9
- 2025Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Datacited by 9
- 2023Federated Learning of Models Pre-Trained on Different Features with Consensus Graphscited by 8
- 2025Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Datacited by 7
- 2025Optimal Transport Depth Up-Scalingcited by 4
- 2023Model-Agnostic Federated Learningcited by 3
- 2023Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectivescited by 3
- 2026Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Datacited by 3
12 most cited of 17 All 17 (JSON)
- 2026Federated Clustering with Unknown Local and Global Cluster Cardinalities29 Sep 2026
- 2026Model Merging as Probabilistic Inference in Fine-Tuning Parameter Space2 Jul 2026
- 2026Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Data6 Feb 2026
- 2025Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Data27 Oct 2025
- 2025Optimal Transport Depth Up-Scaling11 Aug 2025
- 2025Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data27 Feb 2025
- 2023The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning5 Dec 2023
- 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark12 Nov 2023
- 2023Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives5 Jul 2023
- 2023Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs2 Jun 2023
- 2023Efficient Personalized Federated Learning via Sparse Model-Adaptation4 May 2023
- 2023Model-Agnostic Federated Learning8 Mar 2023
12 newest of 17 All 17 (JSON)