Debunking Optimization Myths in Federated Learning for Medical Image Classification

• URL: http://arxiv.org/abs/2507.19822v1
• Date: Sat, 26 Jul 2025 06:41:17 GMT
• Title: Debunking Optimization Myths in Federated Learning for Medical Image Classification
• Authors: Youngjoon Lee, Hyukjoon Lee, Jinu Gong, Yang Cao, Joonhyuk Kang,
• Abstract summary: Federated Learning (FL) is a collaborative learning method that enables decentralized model training while preserving data privacy.<n>Recent FL methods are often sensitive to local factors such as blood blood learning rates, limiting their robustness in practical deployments.<n>We numerically show that the choice of local and learning rate has a greater effect on performance than the specific FL method.
• Score: 6.599230791004658
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) is a collaborative learning method that enables decentralized model training while preserving data privacy. Despite its promise in medical imaging, recent FL methods are often sensitive to local factors such as optimizers and learning rates, limiting their robustness in practical deployments. In this work, we revisit vanilla FL to clarify the impact of edge device configurations, benchmarking recent FL methods on colorectal pathology and blood cell classification task. We numerically show that the choice of local optimizer and learning rate has a greater effect on performance than the specific FL method. Moreover, we find that increasing local training epochs can either enhance or impair convergence, depending on the FL method. These findings indicate that appropriate edge-specific configuration is more crucial than algorithmic complexity for achieving effective FL.

Related papers

• Can We Theoretically Quantify the Impacts of Local Updates on the Generalization Performance of Federated Learning? [50.03434441234569]
  Federated Learning (FL) has gained significant popularity due to its effectiveness in training machine learning models across diverse sites without requiring direct data sharing. While various algorithms have shown that FL with local updates is a communication-efficient distributed learning framework, the generalization performance of FL with local updates has received comparatively less attention.
  arXiv  Detail & Related papers  (2024-09-05T19:00:18Z)
• Federated Active Learning Framework for Efficient Annotation Strategy in Skin-lesion Classification [1.8149633401257899]
  Federated Learning (FL) enables multiple institutes to train models collaboratively without sharing private data. Active learning (AL) has shown promising performance in reducing the number of data annotations in medical image analysis. We propose a federated AL (FedAL) framework in which AL is executed periodically and interactively under FL.
  arXiv  Detail & Related papers  (2024-06-17T08:16:28Z)
• A Survey on Efficient Federated Learning Methods for Foundation Model Training [62.473245910234304]
  Federated Learning (FL) has become an established technique to facilitate privacy-preserving collaborative training across a multitude of clients. In the wake of Foundation Models (FM), the reality is different for many deep learning applications. We discuss the benefits and drawbacks of parameter-efficient fine-tuning (PEFT) for FL applications.
  arXiv  Detail & Related papers  (2024-01-09T10:22:23Z)
• Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence [83.58839320635956]
  Federated Learning (FL) can be used in mobile edge networks to train machine learning models in a distributed manner. Recent FL has been interpreted within a Model-Agnostic Meta-Learning (MAML) framework, which brings FL significant advantages in fast adaptation and convergence over heterogeneous datasets. This paper addresses how much benefit MAML brings to FL and how to maximize such benefit over mobile edge networks.
  arXiv  Detail & Related papers  (2023-03-23T02:42:10Z)
• MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning [92.91544082745196]
  Federated learning (FL) has been widely employed for medical image analysis. FL's performance is limited for multiple sclerosis (MS) lesion segmentation tasks. We propose the first FL MS lesion segmentation framework via two effective re-weighting mechanisms.
  arXiv  Detail & Related papers  (2022-05-03T14:06:03Z)
• Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization [107.72786199113183]
  Federated learning (FL) provides a distributed learning framework for multiple participants to collaborate learning without sharing raw data. In this paper, we propose a novel Split-Mix FL strategy for heterogeneous participants that, once training is done, provides in-situ customization of model sizes and robustness.
  arXiv  Detail & Related papers  (2022-03-18T04:58:34Z)
• Towards Federated Learning on Time-Evolving Heterogeneous Data [13.080665001587281]
  Federated Learning (FL) is an emerging learning paradigm that preserves privacy by ensuring client data locality on edge devices. Despite recent research efforts on improving the optimization of heterogeneous data, the impact of time-evolving heterogeneous data in real-world scenarios has not been well studied. We propose Continual Federated Learning (CFL), a flexible framework, to capture the time-evolving heterogeneity of FL.
  arXiv  Detail & Related papers  (2021-12-25T14:58:52Z)
• Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning [61.488646649045215]
  Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices)
  arXiv  Detail & Related papers  (2021-11-28T19:03:39Z)
• Critical Learning Periods in Federated Learning [11.138980572551066]
  Federated learning (FL) is a popular technique to train machine learning (ML) models with decentralized data. We show that the final test accuracy of FL is dramatically affected by the early phase of the training process.
  arXiv  Detail & Related papers  (2021-09-12T21:06:07Z)
• Federated Learning with Nesterov Accelerated Gradient Momentum Method [47.49442006239034]
  Federated learning (FL) is a fast-developing technique that allows multiple workers to train a global model based on a distributed dataset. It is well known that Nesterov Accelerated Gradient (NAG) is more advantageous in centralized training environment. In this work, we focus on a version of FL based on NAG and provide a detailed convergence analysis.
  arXiv  Detail & Related papers  (2020-09-18T09:38:11Z)

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.