FedFiTS: Fitness-Selected, Slotted Client Scheduling for Trustworthy Federated Learning in Healthcare AI

• URL: http://arxiv.org/abs/2509.19120v1
• Date: Tue, 23 Sep 2025 15:06:04 GMT
• Title: FedFiTS: Fitness-Selected, Slotted Client Scheduling for Trustworthy Federated Learning in Healthcare AI
• Authors: Ferdinand Kahenga, Antoine Bagula, Sajal K. Das, Patrick Sello,
• Abstract summary: Federated Learning (FL) has emerged as a powerful paradigm for privacy-preserving model training, yet deployments in sensitive domains such as healthcare face persistent challenges.<n>This paper introduces FedFiTS, a trust-aware selective FLTS that advances FedFaSt line by combining fitness-based client election with adaptive aggregation.
• Score: 33.17279604575767
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Federated Learning (FL) has emerged as a powerful paradigm for privacy-preserving model training, yet deployments in sensitive domains such as healthcare face persistent challenges from non-IID data, client unreliability, and adversarial manipulation. This paper introduces FedFiTS, a trust and fairness-aware selective FL framework that advances the FedFaSt line by combining fitness-based client election with slotted aggregation. FedFiTS implements a three-phase participation strategy-free-for-all training, natural selection, and slotted team participation-augmented with dynamic client scoring, adaptive thresholding, and cohort-based scheduling to balance convergence efficiency with robustness. A theoretical convergence analysis establishes bounds for both convex and non-convex objectives under standard assumptions, while a communication-complexity analysis shows reductions relative to FedAvg and other baselines. Experiments on diverse datasets-medical imaging (X-ray pneumonia), vision benchmarks (MNIST, FMNIST), and tabular agricultural data (Crop Recommendation)-demonstrate that FedFiTS consistently outperforms FedAvg, FedRand, and FedPow in accuracy, time-to-target, and resilience to poisoning attacks. By integrating trust-aware aggregation with fairness-oriented client selection, FedFiTS advances scalable and secure FL, making it well suited for real-world healthcare and cross-domain deployments.

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