Federated Health Data
Dr. Rafael Gutierrez
LeMay Publishing
HEALTHCARE
Federated Health Data
Health Informatics11,449 words69 chapters
Published by LeMay Publishing. 11,449 words across 69 chapters.
About This Publication
An investigation of federated learning and privacy-preserving computation for clinical datasets, addressing the central paradox of health informatics: advancing knowledge while protecting patient privacy.
Published by LeMay Publishing, a division of LeMay. Massachusetts.
ISBN: 979-8-0000-7022-2
Chapters
1FEDERATED HEALTH DATA
2Federated Learning and Privacy-Preserving Computation for Clinical Datasets
3ABOUT THE AUTHOR
4PREFACE
5TABLE OF CONTENTS
6CHAPTER 1
7THE CLINICAL DATA FRAGMENTATION PROBLEM
81.1 The Scale and Richness of Modern Health Data
91.2 The Persistence of Data Silos
101.3 The Costs of Fragmentation
111.4 The Traditional Approaches and Their Limitations
121.5 The Federated Learning Proposition
13CHAPTER 2
14FOUNDATIONS OF FEDERATED LEARNING
152.1 Formal Definition and Core Concepts
162.2 Communication Efficiency and Compression
172.3 Aggregation Strategies Beyond Federated Averaging
182.4 Convergence Guarantees and Theoretical Foundations
19CHAPTER 3
20ARCHITECTURES FOR FEDERATED CLINICAL SYSTEMS
213.1 Centralized Federated Architecture
223.2 Decentralized and Peer-to-Peer Architectures
233.3 Hierarchical Architectures
243.4 Infrastructure Considerations for Healthcare Deployments
25CHAPTER 4
26PRIVACY-PRESERVING COMPUTATION: DIFFERENTIAL PRIVACY, SECURE MULTI-PARTY COMPUTATION, AND HOMOMORPHIC ENCRYPTION
274.1 The Privacy Challenge in Federated Learning
284.2 Differential Privacy
294.3 Secure Multi-Party Computation
304.4 Homomorphic Encryption
314.5 Integrating Privacy Technologies: Defense in Depth
32CHAPTER 5
33DATA HETEROGENEITY AND NON-IID DISTRIBUTIONS IN CLINICAL SETTINGS
345.1 Sources of Non-IID Data in Healthcare
355.2 Taxonomy of Non-IID Settings
365.3 Algorithmic Approaches to Heterogeneity
375.4 Data Harmonization and Common Data Models
38CHAPTER 6
39FEDERATED LEARNING FOR CLINICAL APPLICATIONS: IMAGING, GENOMICS, AND ELECTRONIC HEALTH RECORDS
406.1 Medical Imaging
416.2 Genomics and Multi-Omics Data
426.3 Electronic Health Records
436.4 Public Health and Epidemiological Surveillance
44CHAPTER 7
45GOVERNANCE, REGULATION, AND INSTITUTIONAL TRUST FRAMEWORKS
467.1 Regulatory Landscape
477.2 Data Use Agreements and Contractual Frameworks
487.3 Institutional Review and Ethics
497.4 Audit and Accountability
50CHAPTER 8
51SECURITY THREAT MODELS AND ADVERSARIAL CONSIDERATIONS
528.1 Threat Taxonomy
538.2 Byzantine-Robust Aggregation
548.3 Backdoor and Data Poisoning Defenses
55CHAPTER 9
56IMPLEMENTATION: FROM PILOT TO PRODUCTION IN HEALTH SYSTEMS
579.1 Readiness Assessment
589.2 Pilot Design and Execution
599.3 Scaling to Production
609.4 Operational Monitoring and Model Lifecycle Management
61CHAPTER 10
62FUTURE DIRECTIONS AND THE EVOLVING LANDSCAPE OF DECENTRALIZED CLINICAL INTELLIGENCE
6310.1 Federated Foundation Models for Healthcare
6410.2 Federated Learning with Multimodal Clinical Data
6510.3 Federated Reinforcement Learning for Treatment Optimization
6610.4 Regulatory Evolution and Standards Development
6710.5 Toward a Federated Health Data Ecosystem
68BIBLIOGRAPHY
69INDEX OF KEY TERMS