From strategy to deployment we build privacy-preserving AI systems

Problem & Data Discovery

• Stakeholder interviews to identify goals
• Define potential ML use cases
• Map data sources across departments (images, tabular, text)
• Analyse data partitioning (horizontal/vertical/hybrid)
• Assess number and role of participating entities

Output: Use case definition & data landscape report

FL Feasibility Analysis

• Analyse data distribution & heterogeneity
• Assess label imbalance impact on model
• Evaluate communication & network constraints
• Review available training infrastructure
• Identify privacy & security requirements

Output: FL feasibility assessment report

Privacy & Security Strategy

• Evaluate privacy-preserving techniques such as Differential Privacy, Homomorphic Encryption etc
• Identify regulatory & compliance constraints
• Recommend privacy-preserving strategies

Output: Privacy architecture recommendation

System Architecture Design

• Define training architecture (centralised coordinator vs decentralised)
• Select appropriate ML models
• Design communication protocols between participants
• Define aggregation strategies (e.g., FedAvg, secure aggregation)

Output: FL architecture blueprint

Implementation Roadmap

• Develop a project timeline with milestones
• Estimate required resources (compute, personnel)
• Provide cost estimates
• Recommend technology stack (frameworks, tools, platforms)

Output: FL deployment roadmap

Implementation Details

• Setup and configuration of FL frameworks (e.g., TensorFlow Federated, PySyft, Flower)
• Development of ML models tailored to the use case
• Integration with existing data infrastructure
• Implementation of privacy-preserving techniques
• Deployment of communication and aggregation pipelines
• Testing, validation and performance optimisation

Operational Support

• Monitoring and maintenance of FL systems
• Debugging and performance tuning
• Documentation and knowledge transfer
• Training workshops for internal teams