TDC-FL: a trust-aware and cluster-driven federated learning framework for heterogeneous healthcare systems

Dynamic Trust-Aware Federated Aggregation for Privacy-Aware Multicentric Healthcare AI

This paper proposes Dynamic Trust-Aware Federated Aggregation (DTAFA), which evaluates each participating site through gradient quality, behavioral consistency, and data integrity, then smooths the resulting trust score across rounds, and combines cohort size, smoothed trust, and a current-round quality gate.

Federated Learning–Driven Trustful and Privacy-Preserving Service Composition in Virtual Hospitals

: The increasing adoption of virtual hospitals and Internet of Medical Things (IoMT)-driven clinical workflows demands service composition mechanisms that can operate reliably under strict privacy constraints and heterogeneous provider ecosystems. Traditional trust-based composition approaches, such as majority voting...

Federated Learning for Privacy-Preserving Collaborative Data Mining in Healthcare

Semantic-Aware Differentially Private and Trust-Driven Robust Federated Optimization for Multi-Center Medical Image Analysis

Federated learning enables hospitals to jointly train diagnostic models without sharing raw medical images, yet multi-center deployment remains vulnerable to site-dependent semantic shifts, privacy leakage, and unreliable model updates. This paper presents a two-level optimization framework. At the client level, SemaDi...

Aggregation Algorithm Selection for Non-IID Federated Learning in Healthcare: Analysis of Convergence, Privacy, and Communication Tradeoffs

With Federated Learning (FL), hospitals are able to train models together without moving patient data, which is important due to security laws such as HIPAA and GDPR that make most deployments legally prohibit training models with patient data in a central location. The larger issue is statistical. Hospitals likely hav...

HDP-FL: A Hierarchical Differential Privacy Framework for Federated Learning in Healthcare

Federated learning (FL) enables collaborative model training in Internet of Things (IoT)-enabled healthcare environments without directly sharing raw medical data. However, shared gradients and model parameters may still expose sensitive information, and uniform privacy budgets cannot reflect different medical-data sen...