Artificial Intelligence in Healthcare
Federated Learning for Privacy-Preserving Medical AI
This dissertation explores a transformative approach to collaborative medical AI, enabling institutions to jointly develop robust diagnostic models while preserving patient privacy and data sovereignty. Focusing on Alzheimer's disease classification using 3D MRI data from ADNI, the research introduces novel methods for realistic data partitioning and adaptive privacy, addressing critical gaps in practical deployment.
Executive Summary
Unlocking Collaborative AI in Healthcare
Our research demonstrates that federated learning, particularly with Adaptive Local Differential Privacy (ALDP) and FedProx, can achieve or surpass centralised training performance for Alzheimer's disease classification. This provides a robust framework for privacy-preserving medical AI deployment, addressing critical challenges in data fragmentation and regulatory compliance.
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ALDP Accuracy (2-Client)
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AD Sensitivity Increase (FedProx)
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Privacy-Utility Gain (ALDP)
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FedProx Peak Accuracy (3-Client)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Novel Methodologies for Realistic Federated Learning
This research introduces a novel site-aware data partitioning strategy that preserves institutional boundaries, enabling realistic assessment of algorithm performance under true multi-institutional collaborations. It also details the Adaptive Local Differential Privacy (ALDP) mechanism, which dynamically adjusts privacy parameters based on training progress and parameter characteristics, significantly improving privacy-utility trade-offs over traditional fixed-noise approaches.
Advanced Federated Learning Algorithms
The study benchmarks core federated learning algorithms: Federated Averaging (FedAvg) for baseline comparison, and Federated Proximal (FedProx) for addressing statistical heterogeneity and client drift in non-IID medical datasets. Additionally, SecAgg+ protocols are evaluated for their cryptographic privacy guarantees, ensuring model parameter confidentiality during aggregation.
Enhancing Privacy with Adaptive Differential Privacy
A systematic exploration of Local Differential Privacy (Local DP) mechanisms for Alzheimer's disease classification is presented, alongside the development of an Adaptive Local Differential Privacy (ALDP) mechanism. ALDP features temporal privacy budget adaptation and per-tensor variance-aware noise scaling, offering significantly improved utility-privacy trade-offs for high-dimensional medical imaging while maintaining formal privacy guarantees.
Enterprise Process Flow: Site-Aware Data Partitioning
Site Analysis and Ranking
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Greedy Client Assignment
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Train-Validation Splitting per Client
This flowchart illustrates the robust, site-aware data partitioning strategy ensuring institutional boundaries are maintained while enabling balanced client participation across federated learning simulations.
80.4%
ALDP Peak Accuracy (ε₀=2000, 2-Client)
The Adaptive Local Differential Privacy (ALDP) mechanism achieved a remarkable 80.4% accuracy, outperforming traditional fixed-noise DP by 5-7 percentage points. This counter-intuitive result highlights ALDP's superior convergence stability and beneficial regularisation effects in limited medical datasets.
| Strategy | Key Features | Performance Highlights |
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| CL (Centralised) | Traditional training on aggregated data, serving as baseline for performance comparison. |
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| FedAvg | Standard federated averaging, simple, communication-efficient. Can struggle with non-IID data. |
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| FedProx | Addresses client heterogeneity with proximal regularization, improving stability with non-IID data. Requires μ tuning. |
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| SecAgg+ | Cryptographic secure aggregation, ensures individual model updates are confidential to server. Reduces communication overhead via quantization. |
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This table summarises the performance of different federated learning strategies in a 3-client scenario, highlighting FedProx's superior ability to manage data heterogeneity and achieve peak accuracy.
Understanding Collaborative Benefits: Individual Client vs. Federated Performance
An ablation study evaluating individual client contributions in a 4-client scenario revealed significant performance variations among institutions when trained in isolation. Client 4 achieved the highest individual performance at 75.4 ± 1.02% accuracy, while Client 1 showed the lowest at 68.2 ± 2.93% accuracy.
However, the centralised training model significantly outperformed all individual clients, with improvements ranging from 4.8 to 12.0 percentage points. This underscores the substantial benefits of federated collaboration, especially for institutions with weaker local datasets, where participation in a federated learning framework can lead to significant diagnostic improvements without compromising data sovereignty.
"Centralised training (80.2 ± 2.23% accuracy) substantially outperformed all individual clients, with improvements ranging from 4.8 percentage points for the strongest client to 12.0 percentage points for the weakest."
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