TriCon-SF: A Triple-Shuffle and Contribution-Aware Serial Federated Learning Framework for Heterogeneous Healthcare Data

TL;DR

TriCon-SF is a novel federated learning framework for healthcare data that enhances privacy, robustness, and contribution evaluation through triple shuffling and Shapley value-based contribution assessment.

Contribution

It introduces a triple-shuffle mechanism combined with contribution-aware evaluation to improve privacy, robustness, and accountability in serial federated learning for healthcare.

Findings

Outperforms standard federated learning in accuracy and communication efficiency.

Enhances privacy and robustness against model inversion and linkage attacks.

Demonstrates resilience against malicious client behaviors in healthcare datasets.

Abstract

Serial pipeline training is an efficient paradigm for handling data heterogeneity in cross-silo federated learning with low communication overhead. However, even without centralized aggregation, direct transfer of models between clients can violate privacy regulations and remain susceptible to gradient leakage and linkage attacks. Additionally, ensuring resilience against semi-honest or malicious clients who may manipulate or misuse received models remains a grand challenge, particularly in privacy-sensitive domains such as healthcare. To address these challenges, we propose TriCon-SF, a novel serial federated learning framework that integrates triple shuffling and contribution awareness. TriCon-SF introduces three levels of randomization by shuffling model layers, data segments, and training sequences to break deterministic learning patterns and disrupt potential attack vectors, thereby enhancing privacy and robustness. In parallel, it leverages Shapley value methods to dynamically evaluate client contributions during training, enabling the detection of dishonest behavior and enhancing system accountability. Extensive experiments on non-IID healthcare datasets demonstrate that TriCon-SF outperforms standard serial and parallel federated learning in both accuracy and communication efficiency. Security analysis further supports its resilience against client-side privacy attacks.