Federated Reinforcement Learning for Privacy-Preserving Smart Healthcare Decision Systems
Keywords:
federated reinforcement learning, privacy-preserving healthcare, smart decision systems, differential privacy, distributed reinforcement learning, clinical infrastructure, fairness, governanceAbstract
The increasing digitization of healthcare has generated vast amounts of sensitive patient data, creating both opportunities for intelligent decision support and significant privacy risks. Traditional reinforcement learning approaches that require centralized data aggregation are often impractical in clinical settings due to legal, ethical, and infrastructural constraints. Federated reinforcement learning offers a paradigm that combines the distributed learning capabilities of federated optimization with the sequential decision-making power of reinforcement learning, enabling multiple healthcare institutions to collaboratively train policies without exchanging raw patient records. This paper presents a systems-level examination of federated reinforcement learning for privacy-preserving smart healthcare decision systems. It explores architectural trade-offs between communication efficiency and model performance, the role of differential privacy and secure aggregation in protecting patient confidentiality, and the infrastructural requirements for real-time deployment in heterogeneous clinical environments. The discussion extends to governance challenges, including fairness across populations with differing data distributions, accountability for autonomous recommendations, and regulatory alignment with frameworks such as HIPAA and GDPR. Robustness considerations are analyzed, focusing on the vulnerability of federated policies to adversarial attacks and distributional shifts. Sustainability aspects, including energy consumption of repeated communication rounds and model life-cycle management, are addressed. The paper concludes by outlining policy implications and future research directions, emphasizing the need for standardized evaluation benchmarks, interpretability mechanisms, and interdisciplinary collaboration to ensure that federated reinforcement learning systems are both effective and ethically sound in healthcare.
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