Asynchronous Proximal Federated Aggregation Framework for Diverse Healthcare Networks
By
Manakkattu Sreelakshmi
Radhakrishnan Delhibabu
July 17, 2026
Clinical Scorecard: Asynchronous Proximal Federated Aggregation Framework for Diverse Healthcare Networks
At a Glance
Category Detail
Condition Federated Learning in Healthcare Key Mechanisms Asynchronous aggregation with local proximal regularization and staleness dampening. Target Population Healthcare institutions utilizing IoMT devices. Care Setting Decentralized healthcare environments.
Key Highlights
APFA framework reduces total wait time by 71% compared to synchronous baselines. Achieved 80% diagnostic viability in 4.1 simulated hours. Addresses computational asymmetry and statistical heterogeneity in healthcare data. Guideline-Based Recommendations
Diagnosis
Utilize federated learning to enhance diagnostic capabilities while preserving patient privacy. Management
Implement asynchronous aggregation strategies to improve model training efficiency. Monitoring & Follow-up
Continuously evaluate model performance across diverse clinical settings. Risks
Mitigate risks of weight divergence and catastrophic forgetting in model training. Patient & Prescribing Data
Patients monitored via IoMT devices.
Federated learning allows for personalized medicine without compromising data privacy.
Clinical Best Practices
Incorporate local training with proximal regularization to enhance model accuracy. Apply staleness-aware aggregation to prevent model corruption from delayed updates. Related Resources & Content
