Surgical Aggregation: Federated Class-Heterogeneous Learning

• URL: http://arxiv.org/abs/2301.06683v5
• Date: Fri, 5 Jan 2024 17:18:56 GMT
• Title: Surgical Aggregation: Federated Class-Heterogeneous Learning
• Authors: Pranav Kulkarni, Adway Kanhere, Paul H. Yi, Vishwa S. Parekh
• Abstract summary: We propose surgical aggregation, a federated learning framework for aggregating knowledge from class-heterogeneous datasets. We evaluate our method using simulated and real-world class-heterogeneous datasets across both independent and identically distributed (iid) and non-iid settings.
• Score: 4.468858802955592
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The release of numerous chest x-ray datasets has spearheaded the development of deep learning models with expert-level performance. However, they have limited interoperability due to class-heterogeneity -- a result of inconsistent labeling schemes and partial annotations. Therefore, it is challenging to leverage these datasets in aggregate to train models with a complete representation of abnormalities that may occur within the thorax. In this work, we propose surgical aggregation, a federated learning framework for aggregating knowledge from class-heterogeneous datasets and learn a model that can simultaneously predict the presence of all disease labels present across the datasets. We evaluate our method using simulated and real-world class-heterogeneous datasets across both independent and identically distributed (iid) and non-iid settings. Our results show that surgical aggregation outperforms current methods, has better generalizability, and is a crucial first step towards tackling class-heterogeneity in federated learning to facilitate the development of clinically-useful models using previously non-interoperable chest x-ray datasets.

Related papers

• FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated Learning [3.7088276910640365]
  Federated learning is a privacy-preserving solution for training distributed datasets across data centers. We propose a novel algorithm aimed at improving the performance of generative models within FL. Experimental results on three public chest X-ray datasets show superior performance in medical image generation.
  arXiv  Detail & Related papers  (2024-12-16T05:43:14Z)
• Fake It Till Make It: Federated Learning with Consensus-Oriented Generation [52.82176415223988]
  We propose federated learning with consensus-oriented generation (FedCOG) FedCOG consists of two key components at the client side: complementary data generation and knowledge-distillation-based model training. Experiments on classical and real-world FL datasets show that FedCOG consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-12-10T18:49:59Z)
• PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learning [55.930403371398114]
  We propose a novel generative adversarial network (GAN) sharing and aggregation strategy for personalized learning (PFL) PFL-GAN addresses the client heterogeneity in different scenarios. More specially, we first learn the similarity among clients and then develop an weighted collaborative data aggregation. The empirical results through the rigorous experimentation on several well-known datasets demonstrate the effectiveness of PFL-GAN.
  arXiv  Detail & Related papers  (2023-08-23T22:38:35Z)
• FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation [32.36334319329364]
  Cross-silo federated learning (FL) enables the development of machine learning models on datasets distributed across data centers. Recent research has found that current FL algorithms face a trade-off between local and global performance when confronted with distribution shifts. We propose a novel federated model soup method to optimize the trade-off between local and global performance.
  arXiv  Detail & Related papers  (2023-07-20T00:07:29Z)
• FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoring [26.07488492998861]
  Federated learning enables medical clients to collaboratively train a deep model without sharing data. We propose federated classifier anchoring (FCA) by adding a personalized classifier at each client to guide and debias the federated model. FCA outperforms the state-of-the-art methods with large margins for federated long-tailed skin lesion classification and intracranial hemorrhage classification.
  arXiv  Detail & Related papers  (2023-05-01T09:36:48Z)
• Intra-class Adaptive Augmentation with Neighbor Correction for Deep Metric Learning [99.14132861655223]
  We propose a novel intra-class adaptive augmentation (IAA) framework for deep metric learning. We reasonably estimate intra-class variations for every class and generate adaptive synthetic samples to support hard samples mining. Our method significantly improves and outperforms the state-of-the-art methods on retrieval performances by 3%-6%.
  arXiv  Detail & Related papers  (2022-11-29T14:52:38Z)
• Scalable Collaborative Learning via Representation Sharing [53.047460465980144]
  Federated learning (FL) and Split Learning (SL) are two frameworks that enable collaborative learning while keeping the data private (on device) In FL, each data holder trains a model locally and releases it to a central server for aggregation. In SL, the clients must release individual cut-layer activations (smashed data) to the server and wait for its response (during both inference and back propagation). In this work, we present a novel approach for privacy-preserving machine learning, where the clients collaborate via online knowledge distillation using a contrastive loss.
  arXiv  Detail & Related papers  (2022-11-20T10:49:22Z)
• Adaptive Personlization in Federated Learning for Highly Non-i.i.d. Data [37.667379000751325]
  Federated learning (FL) is a distributed learning method that offers medical institutes the prospect of collaboration in a global model. In this work, we investigate an adaptive hierarchical clustering method for FL to produce intermediate semi-global models. Our experiments demonstrate significant performance gain in heterogeneous distribution compared to standard FL methods in classification accuracy.
  arXiv  Detail & Related papers  (2022-07-07T17:25:04Z)
• No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data [78.69828864672978]
  A central challenge in training classification models in the real-world federated system is learning with non-IID data. We propose a novel and simple algorithm called Virtual Representations (CCVR), which adjusts the classifier using virtual representations sampled from an approximated ssian mixture model. Experimental results demonstrate that CCVR state-of-the-art performance on popular federated learning benchmarks including CIFAR-10, CIFAR-100, and CINIC-10.
  arXiv  Detail & Related papers  (2021-06-09T12:02:29Z)
• Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation [7.009650174262515]
  Federated learning (FL) enables collaborative model training while preserving each participant's privacy. FedAvg is a standard algorithm that uses fixed weights, often originating from the dataset sizes at each client, to aggregate the distributed learned models on a server during the FL process. In this work, we design a new data-driven approach, namely Auto-FedAvg, where aggregation weights are dynamically adjusted.
  arXiv  Detail & Related papers  (2021-04-20T18:29:44Z)
• Robust Finite Mixture Regression for Heterogeneous Targets [70.19798470463378]
  We propose an FMR model that finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The results show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-12T03:27:07Z)
• Fuzziness-based Spatial-Spectral Class Discriminant Information Preserving Active Learning for Hyperspectral Image Classification [0.456877715768796]
  This work proposes a novel fuzziness-based spatial-spectral within and between for both local and global class discriminant information preserving method. Experimental results on benchmark HSI datasets demonstrate the effectiveness of the FLG method on Generative, Extreme Learning Machine and Sparse Multinomial Logistic Regression.
  arXiv  Detail & Related papers  (2020-05-28T18:58:11Z)

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.