Analysis

What is data analysis?

Data analysis is the process of applying statistical, machine learning, or computational methods to interpret and extract insights from data. In the context of federated learning, analysis involves training, validating, and evaluating models across distributed datasets that remain at their source. This requires adapting traditional analysis methods to work in decentralized, privacy-preserving environments. Federated analysis may include local model training, global model aggregation, distributed evaluation, and interpretability assessments, all while ensuring that raw data never leaves its original location.

Why is data analysis important?

Data analysis is where federated learning delivers its core value: it enables insights to be drawn across sites and datasets without centralizing data. Effective federated analysis allows organizations and researchers to: Build models collaboratively across partners without sharing sensitive data

Discover patterns and relationships that would be hidden in single-site data
Enhance generalizability and reduce bias by incorporating diverse datasets
Support decision-making and policy with evidence from broader, more representative populations
Advance research while maintaining privacy, compliance, and institutional autonomy

Without robust, federated-ready analysis techniques, distributed data cannot be leveraged to its full potential.

What should be considered for data analysis?

To ensure accurate, secure, and reproducible analysis in federated learning, consider the following:

Model Architecture: Choose algorithms that are suitable for federated training (e.g. federated logistic regression, neural networks, or other decentralized learning methods).
Training Strategy: Define how models will be trained and updated (e.g., federated averaging, synchronous vs. asynchronous rounds).
Data Heterogeneity: Account for differences in data distributions across sites (non-IID data), which can impact model performance.
Privacy Enhancements: Integrate privacy-preserving techniques such as secure aggregation, differential privacy, or federated analytics.
Evaluation: Use both local and federated metrics to assess performance. Consider fairness, representativeness, and robustness.
Explainability: Incorporate model interpretability tools where possible, and communicate results to both technical and non-technical stakeholders.
Reproducibility: Log and document all analysis steps, model configurations, and parameters in version-controlled environments.
Collaboration Tools: Use federated-compatible platforms or containers (e.g. Flower, Substra, Fed-BioMed) to facilitate secure collaboration.
FAIR Principles: Share analysis protocols, code, and models in accessible formats, and link to metadata and context.

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