Trust Aware Federated Learning for Secure Bone Healing Stage Interpretation in e-Health

TL;DR

This paper introduces a trust-aware federated learning framework for secure interpretation of bone healing stages in e-health, effectively managing unreliable or adversarial clients to improve model robustness and accuracy.

Contribution

It proposes an adaptive trust score filtering mechanism integrated with federated learning to enhance security and reliability in distributed medical data analysis.

Findings

Adaptive trust management improves training stability.

Filtering low-trust clients enhances predictive performance.

The approach maintains model robustness against adversarial participants.

Abstract

This paper presents a trust aware federated learning (FL) framework for interpreting bone healing stages using spectral features derived from frequency response data. The primary objective is to address the challenge posed by either unreliable or adversarial participants in distributed medical sensing environments. The framework employs a multi-layer perceptron model trained across simulated clients using the Flower FL framework. The proposed approach integrates an Adaptive Trust Score Scaling and Filtering (ATSSSF) mechanism with exponential moving average (EMA) smoothing to assess, validate and filter client contributions.Two trust score smoothing strategies have been investigated, one with a fixed factor and another that adapts according to trust score variability. Clients with low trust are excluded from aggregation and readmitted once their reliability improves, ensuring model integrity while maintaining inclusivity. Standard classification metrics have been used to compare the performance of ATSSSF with the baseline Federated Averaging strategy. Experimental results demonstrate that adaptive trust management can improve both training stability and predictive performance by mitigating the negative effects of compromised clients while retaining robust detection capabilities. The work establishes the feasibility for adaptive trust mechanisms in federated medical sensing and identifies extension to clinical cross silo aggregation as a future research direction.