What question did this study set out to answer?

This work aims to explore the training of models using federated learning in extreme settings with minimal local data.

May 9, 2026Open Access

Federated Learning in Edge Computing Settings in the Medical Domain

Key Points

This work aims to explore the training of models using federated learning in extreme settings with minimal local data.
Empirical evaluation of federated learning strategies including FedAvg, FedProx, FedAdam, FedAdagrad, CFL, and IFCA in Single-Record Per-Client scenarios.
Comparison of federated learning models with centralized training models.
Development of clustering and weighting methods to address challenges in federated learning.
Proposed clustering and weighting methods improve model robustness under Single-Record Per-Client conditions.
Evaluation shows differences in model performance between federated and centralized training methods.
Analysis indicates that label skews significantly affect model accuracy and consistency in SRPC scenario.

Abstract

In the current rapid pace of digitalization, the volume of personal medical data also increases, raising additional privacy and security concerns. A large amount of this sensitive data is stored on mobile devices and often unavailable for collection due to different regulations, such as the GDPR, as well as users’ reluctance to share. Federated Learning (FL) has emerged as a way to train a model in a decentralized manner without sharing raw data. However, in edge computing settings, FL faces major challenges of data heterogeneity and scarcity.This work explores the ability to train a model in an extreme FL setting, namely Single- Record Per-Client FL (SRPC FL), where each participant’s local dataset comprises one record. Such a scenario reflects eHealth applications like remote patient monitoring, fitness tracking, and personalized treatment support, where local data is highly individual and not representative of the global distribution. In our work, we empirically evaluate standard FL strategies for obtaining a global model, such as (FedAvg, FedProx), adaptive optimization methods (FedAdam, FedAdagrad), and clustering-based approaches (CFL, IFCA) in SRPC FL and compare them with models trained in centralized settings. Additionally, we propose clustering and weighting methods that tackle the challenges of this setup. Finally, we provide an extensive analysis of the robustness of the baseline and the proposed methods in SRPC FL conditions under different label skews in the global dataset.

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Federated Learning in Edge Computing Settings in the Medical Domain

Key Points

Abstract

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