Mind the Missing Split: Resolving Feature Heterogeneity in Swarm Learning with Random Forests

• URL: http://arxiv.org/abs/2607.25538v1
• Date: Tue, 28 Jul 2026 10:20:21 GMT
• Title: Mind the Missing Split: Resolving Feature Heterogeneity in Swarm Learning with Random Forests
• Abstract summary: Swarm Learning is a decentralized collaborative learning mechanism that allows multiple organizations to train a shared model without central coordination or direct data sharing.<n>We propose several deterministic and probabilistic inference-time strategies that resolve such missing splits without restricting training to the intersection of features.<n>We evaluate the methods on nine datasets and demonstrate that they outperform both the intersection baseline and locally trained models across a broad range of scenarios.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Swarm Learning is a decentralized collaborative learning mechanism that allows multiple organizations to train a shared model without central coordination or direct data sharing. In typical horizontal Swarm Learning, datasets across sites are usually assumed to share the same feature set. However, in real-world applications, sites often have partially overlapping features because measurements, protocols, and available covariates differ across sites. This feature heterogeneity creates a practical issue for machine learning algorithms such as Random Forests. Specifically, when decision trees are pooled into a global Random Forest, inference at a given site can become ill-defined if a traversal encounters a split on a feature that is not available locally, often forcing organizations to discard site-specific variables upfront. In this paper, we address feature heterogeneity in Swarm Learning with Random Forests under partially overlapping feature spaces. We propose several deterministic and probabilistic inference-time strategies that resolve such missing splits without restricting training to the intersection of features. We evaluate the methods on nine datasets and demonstrate that they outperform both the intersection baseline and locally trained models across a broad range of scenarios.

Related papers

• Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning [83.67663266193308]
  Class-Incremental Learning (CIL) requires a learning system to learn new classes while retaining previously learned knowledge.<n>In real-world scenarios such as autonomous driving, a system trained on urban roads in sunny weather may later need to operate in rural or highway environments.<n>We propose a unified framework to address domain shift and catastrophic forgetting simultaneously.
  arXiv  Detail & Related papers  (2026-05-09T09:49:13Z)
• Just One Byte (per gradient): A Note on Low-Bandwidth Decentralized Language Model Finetuning Using Shared Randomness [86.61582747039053]
  Language model training in distributed settings is limited by the communication cost of exchanges. We extend recent work using shared randomness to perform distributed fine-tuning with low bandwidth.
  arXiv  Detail & Related papers  (2023-06-16T17:59:51Z)
• Plug In and Learn: Federated Intelligence over a Smart Grid of Models [0.0]
  We present a model-agnostic federated learning method that mirrors the operation of a smart power grid.<n> diverse local models, like energy prosumers, train independently on their own data while exchanging lightweight signals to coordinate with statistically similar peers.<n>This coordination is governed by a graph-based regularizer that encourages connected models to produce similar predictions on a shared, public unlabeled dataset.
  arXiv  Detail & Related papers  (2023-02-08T22:55:57Z)
• Personalizing Federated Medical Image Segmentation via Local Calibration [9.171482226385551]
  Using a single model to adapt to various data distributions from different sites is extremely challenging. We propose a personalized federated framework with textbfLocal textbfCalibration (LC-Fed) to leverage the inter-site in-consistencies. Our method consistently shows superior performance to the state-of-the-art personalized FL methods.
  arXiv  Detail & Related papers  (2022-07-11T06:30:31Z)
• Unsupervised learning of features and object boundaries from local prediction [0.0]
  We introduce a layer of feature maps with a pairwise Markov random field model in which each factor is paired with an additional binary variable, which switches the factor on or off. We can learn both the features and the parameters of the Markov random field factors from images without further supervision signals. We show that computing predictions across space aids both segmentation and feature learning, and models trained to optimize these predictions show similarities to the human visual system.
  arXiv  Detail & Related papers  (2022-05-27T18:54:10Z)
• Random Similarity Forests [2.3204178451683264]
  We propose a classification method capable of handling datasets with features of arbitrary data types while retaining each feature's characteristic. The proposed algorithm, called Random Similarity Forest, uses multiple domain-specific distance measures to combine the predictive performance of Random Forests with the flexibility of Similarity Forests. We show that Random Similarity Forests are on par with Random Forests on numerical data and outperform them on datasets from complex or mixed data domains.
  arXiv  Detail & Related papers  (2022-04-11T20:14:05Z)
• Leveraging Ensembles and Self-Supervised Learning for Fully-Unsupervised Person Re-Identification and Text Authorship Attribution [77.85461690214551]
  Learning from fully-unlabeled data is challenging in Multimedia Forensics problems, such as Person Re-Identification and Text Authorship Attribution. Recent self-supervised learning methods have shown to be effective when dealing with fully-unlabeled data in cases where the underlying classes have significant semantic differences. We propose a strategy to tackle Person Re-Identification and Text Authorship Attribution by enabling learning from unlabeled data even when samples from different classes are not prominently diverse.
  arXiv  Detail & Related papers  (2022-02-07T13:08:11Z)
• The interplay between ranking and communities in networks [0.0]
  We present a generative model based on an interplay between community and hierarchical structures. It assumes that each node has a preference in the interaction mechanism and nodes with the same preference are more likely to interact. We demonstrate our method on synthetic and real-world data and compare performance with two standard approaches for community detection and ranking extraction.
  arXiv  Detail & Related papers  (2021-12-23T16:10:28Z)
• Federated Learning from Small Datasets [48.879172201462445]
  Federated learning allows multiple parties to collaboratively train a joint model without sharing local data. We propose a novel approach that intertwines model aggregations with permutations of local models. The permutations expose each local model to a daisy chain of local datasets resulting in more efficient training in data-sparse domains.
  arXiv  Detail & Related papers  (2021-10-07T13:49:23Z)
• Decentralized Local Stochastic Extra-Gradient for Variational Inequalities [125.62877849447729]
  We consider distributed variational inequalities (VIs) on domains with the problem data that is heterogeneous (non-IID) and distributed across many devices. We make a very general assumption on the computational network that covers the settings of fully decentralized calculations. We theoretically analyze its convergence rate in the strongly-monotone, monotone, and non-monotone settings.
  arXiv  Detail & Related papers  (2021-06-15T17:45:51Z)
• Clustered Federated Learning via Generalized Total Variation Minimization [83.26141667853057]
  We study optimization methods to train local (or personalized) models for local datasets with a decentralized network structure. Our main conceptual contribution is to formulate federated learning as total variation minimization (GTV) Our main algorithmic contribution is a fully decentralized federated learning algorithm.
  arXiv  Detail & Related papers  (2021-05-26T18:07:19Z)
• Exploiting Shared Representations for Personalized Federated Learning [54.65133770989836]
  We propose a novel federated learning framework and algorithm for learning a shared data representation across clients and unique local heads for each client. Our algorithm harnesses the distributed computational power across clients to perform many local-updates with respect to the low-dimensional local parameters for every update of the representation. This result is of interest beyond federated learning to a broad class of problems in which we aim to learn a shared low-dimensional representation among data distributions.
  arXiv  Detail & Related papers  (2021-02-14T05:36:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.