cs.LGSep 25, 2026

Uncertainty-Aware Federated Learning for Infant Movement Analysis

Authors: Edmond S. L. Ho

Organizations: School of Computing Science University of Glasgow Glasgow, Scotland, United Kingdom

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Abstract

Infant movement analysis provides valuable biomarkers for the early identification of neurodevelopmental disorders. Recent advances in deep learning have enabled automated analysis of infant movements from video-derived skeletal representations, achieving performance comparable to expert assessment for tasks such as General Movement Assessment (GMA). However, most existing approaches rely on centralized training, requiring data from multiple institutions to be collected and stored at a single site. Such assumptions are often impractical in clinical settings due to privacy, governance, and data-sharing constraints. To address these challenges, we present, to the best of our knowledge, the first federated learning framework for automated infant movement analysis and General Movement Assessment using skeletal motion data. As a clinically relevant use case, the proposed framework is evaluated on fidgety movement classification. To quantify model confidence, Monte Carlo (MC) Dropout is employed to estimate predictive uncertainty during inference. Building upon this, we propose an Uncertainty-Aware Federated Averaging (UA-FedAvg) strategy that incorporates predictive entropy derived from MC-Dropout into the federated aggregation process, enabling client contributions to be adjusted according to their predictive uncertainty. Experiments were conducted using a cross-subject evaluation protocol under a three-client federated learning setting. Results demonstrate that federated learning substantially improves classification performance compared with independently trained local models while achieving performance approaching that of centralized training. Furthermore, UA-FedAvg and its variant incorporating validation loss generally outperform conventional FedAvg across the evaluated data-split configurations.

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Fig. 1: The overview of the proposed federated learning framework for multi-client General Movement Assessment model training.
Fig. 2: The architecture of the CNN for skeletal motion classification trained at each client.
 Hi=1N∑x∈VH(x)H_{i}=\frac{1}{N}\sum_{x\in V}H(x) 
Algorithm 1 Client-Side MC-Dropout Uncertainty Estimation
MethodData SplitAccuracy ↑\uparrowSensitivity ↑\uparrowSpecificity ↑\uparrowF1 Score ↑\uparrowPredictive Uncertainty ↓\downarrow
AverageMedianStd. Dev
Centralized–0.86850.88550.84680.88300.06680.05780.0553
No Federation111-2110.78180.84620.69990.81320.08260.07450.0640
FedAvg0.85310.88590.81140.87110.09380.06150.0942
UA-FedAvg0.86260.88280.83670.87810.09130.05960.0910
UA-FedAvg w/ vLoss0.86380.88510.83670.87920.09160.06370.0865
TABLE I: Classification performance and predictive uncertainty on the combined results with different data splits.
Data SplitClientMethodAccuracy ↑\uparrowSensitivity ↑\uparrowSpecificity ↑\uparrowF1 Score ↑\uparrowPredictive Uncertainty ↓\downarrow
AverageMedianStd. Dev
111-211#1No Federation0.69760.85530.58230.70600.08600.07810.0682
FedAvg0.84840.90290.80860.83450.09050.05250.0962
UA-FedAvg0.86470.90110.83800.84940.08740.05710.0875
UA-FedAvg w/ vLoss0.86850.90480.84200.85410.08650.05380.0864
#2No Federation0.82450.82360.82680.87260.07760.06970.0581
TABLE II: Classification performance and predictive uncertainty for each client on different dataset splits.
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